{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas  as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import accuracy_score\n",
    "plt.rcParams[\"font.sans-serif\"] = [\"SimHei\"]\n",
    "plt.rcParams[\"axes.unicode_minus\"] = False\n",
    "import random"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "outputs": [
    {
     "data": {
      "text/plain": "          Stu_Age       City  money\nStu_Name                           \nJohn           23  Xianggang   4500\nAlice          26    Beijing   6500\nBob            33   Hangzhou   5550\nJane           30     Daqing   7050\nMike           24    HaErbin   5205\nLisa           35     Berlin   6660\nTom            30    Wenquan   5300\nEmma           36    Toronto   5800\nAlex           26     Moscow   4900\nKate           21      Dubai   5805",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Stu_Age</th>\n      <th>City</th>\n      <th>money</th>\n    </tr>\n    <tr>\n      <th>Stu_Name</th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>John</th>\n      <td>23</td>\n      <td>Xianggang</td>\n      <td>4500</td>\n    </tr>\n    <tr>\n      <th>Alice</th>\n      <td>26</td>\n      <td>Beijing</td>\n      <td>6500</td>\n    </tr>\n    <tr>\n      <th>Bob</th>\n      <td>33</td>\n      <td>Hangzhou</td>\n      <td>5550</td>\n    </tr>\n    <tr>\n      <th>Jane</th>\n      <td>30</td>\n      <td>Daqing</td>\n      <td>7050</td>\n    </tr>\n    <tr>\n      <th>Mike</th>\n      <td>24</td>\n      <td>HaErbin</td>\n      <td>5205</td>\n    </tr>\n    <tr>\n      <th>Lisa</th>\n      <td>35</td>\n      <td>Berlin</td>\n      <td>6660</td>\n    </tr>\n    <tr>\n      <th>Tom</th>\n      <td>30</td>\n      <td>Wenquan</td>\n      <td>5300</td>\n    </tr>\n    <tr>\n      <th>Emma</th>\n      <td>36</td>\n      <td>Toronto</td>\n      <td>5800</td>\n    </tr>\n    <tr>\n      <th>Alex</th>\n      <td>26</td>\n      <td>Moscow</td>\n      <td>4900</td>\n    </tr>\n    <tr>\n      <th>Kate</th>\n      <td>21</td>\n      <td>Dubai</td>\n      <td>5805</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"stu.csv\",index_col=0)\n",
    "df"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "outputs": [
    {
     "data": {
      "text/plain": "          Stu_Age       City  money   Yonns\nStu_Name                                   \nJohn           23  Xianggang   4500  5400.0\nAlice          26    Beijing   6500  7800.0\nBob            33   Hangzhou   5550  6660.0\nJane           30     Daqing   7050  8460.0\nMike           24    HaErbin   5205  6246.0\nLisa           35     Berlin   6660  7992.0\nTom            30    Wenquan   5300  6360.0\nEmma           36    Toronto   5800  6960.0\nAlex           26     Moscow   4900  5880.0\nKate           21      Dubai   5805  6966.0",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Stu_Age</th>\n      <th>City</th>\n      <th>money</th>\n      <th>Yonns</th>\n    </tr>\n    <tr>\n      <th>Stu_Name</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>John</th>\n      <td>23</td>\n      <td>Xianggang</td>\n      <td>4500</td>\n      <td>5400.0</td>\n    </tr>\n    <tr>\n      <th>Alice</th>\n      <td>26</td>\n      <td>Beijing</td>\n      <td>6500</td>\n      <td>7800.0</td>\n    </tr>\n    <tr>\n      <th>Bob</th>\n      <td>33</td>\n      <td>Hangzhou</td>\n      <td>5550</td>\n      <td>6660.0</td>\n    </tr>\n    <tr>\n      <th>Jane</th>\n      <td>30</td>\n      <td>Daqing</td>\n      <td>7050</td>\n      <td>8460.0</td>\n    </tr>\n    <tr>\n      <th>Mike</th>\n      <td>24</td>\n      <td>HaErbin</td>\n      <td>5205</td>\n      <td>6246.0</td>\n    </tr>\n    <tr>\n      <th>Lisa</th>\n      <td>35</td>\n      <td>Berlin</td>\n      <td>6660</td>\n      <td>7992.0</td>\n    </tr>\n    <tr>\n      <th>Tom</th>\n      <td>30</td>\n      <td>Wenquan</td>\n      <td>5300</td>\n      <td>6360.0</td>\n    </tr>\n    <tr>\n      <th>Emma</th>\n      <td>36</td>\n      <td>Toronto</td>\n      <td>5800</td>\n      <td>6960.0</td>\n    </tr>\n    <tr>\n      <th>Alex</th>\n      <td>26</td>\n      <td>Moscow</td>\n      <td>4900</td>\n      <td>5880.0</td>\n    </tr>\n    <tr>\n      <th>Kate</th>\n      <td>21</td>\n      <td>Dubai</td>\n      <td>5805</td>\n      <td>6966.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Yonns\"] = df[\"money\"]*1.2\n",
    "df"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "outputs": [
    {
     "data": {
      "text/plain": "          Stu_Age       City  money   Yonns age_box\nStu_Name                                           \nJohn           23  Xianggang   4500  5400.0     年轻人\nAlice          26    Beijing   6500  7800.0     年轻人\nBob            33   Hangzhou   5550  6660.0     青年人\nJane           30     Daqing   7050  8460.0     年轻人\nMike           24    HaErbin   5205  6246.0     年轻人\nLisa           35     Berlin   6660  7992.0     青年人\nTom            30    Wenquan   5300  6360.0     年轻人\nEmma           36    Toronto   5800  6960.0     青年人\nAlex           26     Moscow   4900  5880.0     年轻人\nKate           21      Dubai   5805  6966.0     年轻人",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Stu_Age</th>\n      <th>City</th>\n      <th>money</th>\n      <th>Yonns</th>\n      <th>age_box</th>\n    </tr>\n    <tr>\n      <th>Stu_Name</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>John</th>\n      <td>23</td>\n      <td>Xianggang</td>\n      <td>4500</td>\n      <td>5400.0</td>\n      <td>年轻人</td>\n    </tr>\n    <tr>\n      <th>Alice</th>\n      <td>26</td>\n      <td>Beijing</td>\n      <td>6500</td>\n      <td>7800.0</td>\n      <td>年轻人</td>\n    </tr>\n    <tr>\n      <th>Bob</th>\n      <td>33</td>\n      <td>Hangzhou</td>\n      <td>5550</td>\n      <td>6660.0</td>\n      <td>青年人</td>\n    </tr>\n    <tr>\n      <th>Jane</th>\n      <td>30</td>\n      <td>Daqing</td>\n      <td>7050</td>\n      <td>8460.0</td>\n      <td>年轻人</td>\n    </tr>\n    <tr>\n      <th>Mike</th>\n      <td>24</td>\n      <td>HaErbin</td>\n      <td>5205</td>\n      <td>6246.0</td>\n      <td>年轻人</td>\n    </tr>\n    <tr>\n      <th>Lisa</th>\n      <td>35</td>\n      <td>Berlin</td>\n      <td>6660</td>\n      <td>7992.0</td>\n      <td>青年人</td>\n    </tr>\n    <tr>\n      <th>Tom</th>\n      <td>30</td>\n      <td>Wenquan</td>\n      <td>5300</td>\n      <td>6360.0</td>\n      <td>年轻人</td>\n    </tr>\n    <tr>\n      <th>Emma</th>\n      <td>36</td>\n      <td>Toronto</td>\n      <td>5800</td>\n      <td>6960.0</td>\n      <td>青年人</td>\n    </tr>\n    <tr>\n      <th>Alex</th>\n      <td>26</td>\n      <td>Moscow</td>\n      <td>4900</td>\n      <td>5880.0</td>\n      <td>年轻人</td>\n    </tr>\n    <tr>\n      <th>Kate</th>\n      <td>21</td>\n      <td>Dubai</td>\n      <td>5805</td>\n      <td>6966.0</td>\n      <td>年轻人</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bins=[0,30,60,100]\n",
    "lables=['年轻人','青年人','老年人']\n",
    "df[\"age_box\"] = pd.cut(df[\"Stu_Age\"],bins=bins,labels=lables)\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "outputs": [
    {
     "data": {
      "text/plain": "age_box  City     \n年轻人      Beijing      6500\n         Berlin          0\n         Daqing       7050\n         Dubai        5805\n         HaErbin      5205\n         Hangzhou        0\n         Moscow       4900\n         Toronto         0\n         Wenquan      5300\n         Xianggang    4500\n青年人      Beijing         0\n         Berlin       6660\n         Daqing          0\n         Dubai           0\n         HaErbin         0\n         Hangzhou     5550\n         Moscow          0\n         Toronto      5800\n         Wenquan         0\n         Xianggang       0\n老年人      Beijing         0\n         Berlin          0\n         Daqing          0\n         Dubai           0\n         HaErbin         0\n         Hangzhou        0\n         Moscow          0\n         Toronto         0\n         Wenquan         0\n         Xianggang       0\nName: money, dtype: int64"
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result = df.groupby([\"age_box\",\"City\"])[\"money\"].sum()\n",
    "result"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "'value' must be an instance of str or bytes, not a tuple",
     "output_type": "error",
     "traceback": [
      "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m",
      "\u001B[1;31mTypeError\u001B[0m                                 Traceback (most recent call last)",
      "Cell \u001B[1;32mIn[25], line 1\u001B[0m\n\u001B[1;32m----> 1\u001B[0m \u001B[43mplt\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mbar\u001B[49m\u001B[43m(\u001B[49m\u001B[43mresult\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mindex\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mvalues\u001B[49m\u001B[43m,\u001B[49m\u001B[43mresult\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mvalues\u001B[49m\u001B[43m)\u001B[49m\n",
      "File \u001B[1;32mF:\\python38\\lib\\site-packages\\matplotlib\\pyplot.py:2387\u001B[0m, in \u001B[0;36mbar\u001B[1;34m(x, height, width, bottom, align, data, **kwargs)\u001B[0m\n\u001B[0;32m   2383\u001B[0m \u001B[38;5;129m@_copy_docstring_and_deprecators\u001B[39m(Axes\u001B[38;5;241m.\u001B[39mbar)\n\u001B[0;32m   2384\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21mbar\u001B[39m(\n\u001B[0;32m   2385\u001B[0m         x, height, width\u001B[38;5;241m=\u001B[39m\u001B[38;5;241m0.8\u001B[39m, bottom\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mNone\u001B[39;00m, \u001B[38;5;241m*\u001B[39m, align\u001B[38;5;241m=\u001B[39m\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mcenter\u001B[39m\u001B[38;5;124m'\u001B[39m,\n\u001B[0;32m   2386\u001B[0m         data\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mNone\u001B[39;00m, \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mkwargs):\n\u001B[1;32m-> 2387\u001B[0m     \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43mgca\u001B[49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mbar\u001B[49m\u001B[43m(\u001B[49m\n\u001B[0;32m   2388\u001B[0m \u001B[43m        \u001B[49m\u001B[43mx\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mheight\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mwidth\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mwidth\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mbottom\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mbottom\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43malign\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43malign\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m   2389\u001B[0m \u001B[43m        \u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43m(\u001B[49m\u001B[43m{\u001B[49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43mdata\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m:\u001B[49m\u001B[43m \u001B[49m\u001B[43mdata\u001B[49m\u001B[43m}\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;28;43;01mif\u001B[39;49;00m\u001B[43m \u001B[49m\u001B[43mdata\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;129;43;01mis\u001B[39;49;00m\u001B[43m \u001B[49m\u001B[38;5;129;43;01mnot\u001B[39;49;00m\u001B[43m \u001B[49m\u001B[38;5;28;43;01mNone\u001B[39;49;00m\u001B[43m \u001B[49m\u001B[38;5;28;43;01melse\u001B[39;49;00m\u001B[43m \u001B[49m\u001B[43m{\u001B[49m\u001B[43m}\u001B[49m\u001B[43m)\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43mkwargs\u001B[49m\u001B[43m)\u001B[49m\n",
      "File \u001B[1;32mF:\\python38\\lib\\site-packages\\matplotlib\\__init__.py:1412\u001B[0m, in \u001B[0;36m_preprocess_data.<locals>.inner\u001B[1;34m(ax, data, *args, **kwargs)\u001B[0m\n\u001B[0;32m   1409\u001B[0m \u001B[38;5;129m@functools\u001B[39m\u001B[38;5;241m.\u001B[39mwraps(func)\n\u001B[0;32m   1410\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21minner\u001B[39m(ax, \u001B[38;5;241m*\u001B[39margs, data\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mNone\u001B[39;00m, \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mkwargs):\n\u001B[0;32m   1411\u001B[0m     \u001B[38;5;28;01mif\u001B[39;00m data \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m:\n\u001B[1;32m-> 1412\u001B[0m         \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43mfunc\u001B[49m\u001B[43m(\u001B[49m\u001B[43max\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[38;5;28;43mmap\u001B[39;49m\u001B[43m(\u001B[49m\u001B[43msanitize_sequence\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43margs\u001B[49m\u001B[43m)\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43mkwargs\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m   1414\u001B[0m     bound \u001B[38;5;241m=\u001B[39m new_sig\u001B[38;5;241m.\u001B[39mbind(ax, \u001B[38;5;241m*\u001B[39margs, \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mkwargs)\n\u001B[0;32m   1415\u001B[0m     auto_label \u001B[38;5;241m=\u001B[39m (bound\u001B[38;5;241m.\u001B[39marguments\u001B[38;5;241m.\u001B[39mget(label_namer)\n\u001B[0;32m   1416\u001B[0m                   \u001B[38;5;129;01mor\u001B[39;00m bound\u001B[38;5;241m.\u001B[39mkwargs\u001B[38;5;241m.\u001B[39mget(label_namer))\n",
      "File \u001B[1;32mF:\\python38\\lib\\site-packages\\matplotlib\\axes\\_axes.py:2317\u001B[0m, in \u001B[0;36mAxes.bar\u001B[1;34m(self, x, height, width, bottom, align, **kwargs)\u001B[0m\n\u001B[0;32m   2314\u001B[0m         x \u001B[38;5;241m=\u001B[39m \u001B[38;5;241m0\u001B[39m\n\u001B[0;32m   2316\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m orientation \u001B[38;5;241m==\u001B[39m \u001B[38;5;124m'\u001B[39m\u001B[38;5;124mvertical\u001B[39m\u001B[38;5;124m'\u001B[39m:\n\u001B[1;32m-> 2317\u001B[0m     \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_process_unit_info\u001B[49m\u001B[43m(\u001B[49m\n\u001B[0;32m   2318\u001B[0m \u001B[43m        \u001B[49m\u001B[43m[\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43mx\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mx\u001B[49m\u001B[43m)\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43my\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mheight\u001B[49m\u001B[43m)\u001B[49m\u001B[43m]\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mkwargs\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mconvert\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;28;43;01mFalse\u001B[39;49;00m\u001B[43m)\u001B[49m\n\u001B[0;32m   2319\u001B[0m     \u001B[38;5;28;01mif\u001B[39;00m log:\n\u001B[0;32m   2320\u001B[0m         \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mset_yscale(\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mlog\u001B[39m\u001B[38;5;124m'\u001B[39m, nonpositive\u001B[38;5;241m=\u001B[39m\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mclip\u001B[39m\u001B[38;5;124m'\u001B[39m)\n",
      "File \u001B[1;32mF:\\python38\\lib\\site-packages\\matplotlib\\axes\\_base.py:2521\u001B[0m, in \u001B[0;36m_AxesBase._process_unit_info\u001B[1;34m(self, datasets, kwargs, convert)\u001B[0m\n\u001B[0;32m   2519\u001B[0m     \u001B[38;5;66;03m# Update from data if axis is already set but no unit is set yet.\u001B[39;00m\n\u001B[0;32m   2520\u001B[0m     \u001B[38;5;28;01mif\u001B[39;00m axis \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m \u001B[38;5;129;01mand\u001B[39;00m data \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m \u001B[38;5;129;01mand\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m axis\u001B[38;5;241m.\u001B[39mhave_units():\n\u001B[1;32m-> 2521\u001B[0m         \u001B[43maxis\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mupdate_units\u001B[49m\u001B[43m(\u001B[49m\u001B[43mdata\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m   2522\u001B[0m \u001B[38;5;28;01mfor\u001B[39;00m axis_name, axis \u001B[38;5;129;01min\u001B[39;00m axis_map\u001B[38;5;241m.\u001B[39mitems():\n\u001B[0;32m   2523\u001B[0m     \u001B[38;5;66;03m# Return if no axis is set.\u001B[39;00m\n\u001B[0;32m   2524\u001B[0m     \u001B[38;5;28;01mif\u001B[39;00m axis \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m:\n",
      "File \u001B[1;32mF:\\python38\\lib\\site-packages\\matplotlib\\axis.py:1449\u001B[0m, in \u001B[0;36mAxis.update_units\u001B[1;34m(self, data)\u001B[0m\n\u001B[0;32m   1447\u001B[0m neednew \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mconverter \u001B[38;5;241m!=\u001B[39m converter\n\u001B[0;32m   1448\u001B[0m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mconverter \u001B[38;5;241m=\u001B[39m converter\n\u001B[1;32m-> 1449\u001B[0m default \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mconverter\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mdefault_units\u001B[49m\u001B[43m(\u001B[49m\u001B[43mdata\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[43m)\u001B[49m\n\u001B[0;32m   1450\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m default \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m \u001B[38;5;129;01mand\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39munits \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m:\n\u001B[0;32m   1451\u001B[0m     \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mset_units(default)\n",
      "File \u001B[1;32mF:\\python38\\lib\\site-packages\\matplotlib\\category.py:116\u001B[0m, in \u001B[0;36mStrCategoryConverter.default_units\u001B[1;34m(data, axis)\u001B[0m\n\u001B[0;32m    114\u001B[0m \u001B[38;5;66;03m# the conversion call stack is default_units -> axis_info -> convert\u001B[39;00m\n\u001B[0;32m    115\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m axis\u001B[38;5;241m.\u001B[39munits \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m:\n\u001B[1;32m--> 116\u001B[0m     axis\u001B[38;5;241m.\u001B[39mset_units(\u001B[43mUnitData\u001B[49m\u001B[43m(\u001B[49m\u001B[43mdata\u001B[49m\u001B[43m)\u001B[49m)\n\u001B[0;32m    117\u001B[0m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[0;32m    118\u001B[0m     axis\u001B[38;5;241m.\u001B[39munits\u001B[38;5;241m.\u001B[39mupdate(data)\n",
      "File \u001B[1;32mF:\\python38\\lib\\site-packages\\matplotlib\\category.py:192\u001B[0m, in \u001B[0;36mUnitData.__init__\u001B[1;34m(self, data)\u001B[0m\n\u001B[0;32m    190\u001B[0m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_counter \u001B[38;5;241m=\u001B[39m itertools\u001B[38;5;241m.\u001B[39mcount()\n\u001B[0;32m    191\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m data \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m:\n\u001B[1;32m--> 192\u001B[0m     \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mupdate\u001B[49m\u001B[43m(\u001B[49m\u001B[43mdata\u001B[49m\u001B[43m)\u001B[49m\n",
      "File \u001B[1;32mF:\\python38\\lib\\site-packages\\matplotlib\\category.py:227\u001B[0m, in \u001B[0;36mUnitData.update\u001B[1;34m(self, data)\u001B[0m\n\u001B[0;32m    224\u001B[0m convertible \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;01mTrue\u001B[39;00m\n\u001B[0;32m    225\u001B[0m \u001B[38;5;28;01mfor\u001B[39;00m val \u001B[38;5;129;01min\u001B[39;00m OrderedDict\u001B[38;5;241m.\u001B[39mfromkeys(data):\n\u001B[0;32m    226\u001B[0m     \u001B[38;5;66;03m# OrderedDict just iterates over unique values in data.\u001B[39;00m\n\u001B[1;32m--> 227\u001B[0m     \u001B[43m_api\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mcheck_isinstance\u001B[49m\u001B[43m(\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;28;43mstr\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;28;43mbytes\u001B[39;49m\u001B[43m)\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mvalue\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mval\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m    228\u001B[0m     \u001B[38;5;28;01mif\u001B[39;00m convertible:\n\u001B[0;32m    229\u001B[0m         \u001B[38;5;66;03m# this will only be called so long as convertible is True.\u001B[39;00m\n\u001B[0;32m    230\u001B[0m         convertible \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_str_is_convertible(val)\n",
      "File \u001B[1;32mF:\\python38\\lib\\site-packages\\matplotlib\\_api\\__init__.py:93\u001B[0m, in \u001B[0;36mcheck_isinstance\u001B[1;34m(_types, **kwargs)\u001B[0m\n\u001B[0;32m     91\u001B[0m     names\u001B[38;5;241m.\u001B[39mremove(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mNone\u001B[39m\u001B[38;5;124m\"\u001B[39m)\n\u001B[0;32m     92\u001B[0m     names\u001B[38;5;241m.\u001B[39mappend(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mNone\u001B[39m\u001B[38;5;124m\"\u001B[39m)\n\u001B[1;32m---> 93\u001B[0m \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mTypeError\u001B[39;00m(\n\u001B[0;32m     94\u001B[0m     \u001B[38;5;124m\"\u001B[39m\u001B[38;5;132;01m{!r}\u001B[39;00m\u001B[38;5;124m must be an instance of \u001B[39m\u001B[38;5;132;01m{}\u001B[39;00m\u001B[38;5;124m, not a \u001B[39m\u001B[38;5;132;01m{}\u001B[39;00m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;241m.\u001B[39mformat(\n\u001B[0;32m     95\u001B[0m         k,\n\u001B[0;32m     96\u001B[0m         \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124m, \u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;241m.\u001B[39mjoin(names[:\u001B[38;5;241m-\u001B[39m\u001B[38;5;241m1\u001B[39m]) \u001B[38;5;241m+\u001B[39m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124m or \u001B[39m\u001B[38;5;124m\"\u001B[39m \u001B[38;5;241m+\u001B[39m names[\u001B[38;5;241m-\u001B[39m\u001B[38;5;241m1\u001B[39m]\n\u001B[0;32m     97\u001B[0m         \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mlen\u001B[39m(names) \u001B[38;5;241m>\u001B[39m \u001B[38;5;241m1\u001B[39m \u001B[38;5;28;01melse\u001B[39;00m names[\u001B[38;5;241m0\u001B[39m],\n\u001B[0;32m     98\u001B[0m         type_name(\u001B[38;5;28mtype\u001B[39m(v))))\n",
      "\u001B[1;31mTypeError\u001B[0m: 'value' must be an instance of str or bytes, not a tuple"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": "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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar(result.index.values,result.values)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for index,x in enumerate(result.index.values):\n",
    "\tplt.bar(\"\".join(str(x)),result.values[index])\n",
    "plt.xticks(rotation=-45)\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "outputs": [
    {
     "data": {
      "text/plain": "         id               start_time           oid           pid  \\\n0         0  2020-04-24 11:50:39 UTC  2.294360e+18  1.515970e+18   \n1         1  2020-04-24 11:50:39 UTC  2.294360e+18  1.515970e+18   \n2         2  2020-04-24 14:37:43 UTC  2.294440e+18  2.273950e+18   \n3         3  2020-04-24 14:37:43 UTC  2.294440e+18  2.273950e+18   \n4         4  2020-04-24 19:16:21 UTC  2.294580e+18  2.273950e+18   \n...     ...                      ...           ...           ...   \n9995  10001  2020-05-17 08:21:59 UTC  2.310920e+18  2.273950e+18   \n9996  10002  2020-05-17 08:23:45 UTC  2.310930e+18  2.273950e+18   \n9997  10003  2020-05-17 08:24:07 UTC  2.310930e+18  1.515970e+18   \n9998  10004  2020-05-17 08:28:58 UTC  2.310930e+18  1.515970e+18   \n9999  10005  2020-05-17 08:28:58 UTC  2.310930e+18  1.515970e+18   \n\n           type_id                         type     brand   price  \\\n0     2.270000e+18           electronics.tablet   samsung  162.01   \n1     2.270000e+18           electronics.tablet   samsung  162.01   \n2     2.270000e+18  electronics.audio.headphone    huawei   77.52   \n3     2.270000e+18  electronics.audio.headphone    huawei   77.52   \n4     2.270000e+18                          NaN   karcher  217.57   \n...            ...                          ...       ...     ...   \n9995  2.270000e+18   appliances.environment.fan   polaris   20.81   \n9996  2.370000e+18         electronics.video.tv   samsung  416.64   \n9997  2.270000e+18           computers.notebook      asus  509.24   \n9998  2.270000e+18                          NaN  coolinar    3.22   \n9999  2.270000e+18                          NaN  coolinar    3.22   \n\n               uid  age sex province  \n0     1.520000e+18   24   女       海南  \n1     1.520000e+18   24   女       海南  \n2     1.520000e+18   38   女       北京  \n3     1.520000e+18   38   女       北京  \n4     1.520000e+18   32   女       广东  \n...            ...  ...  ..      ...  \n9995  1.520000e+18   24   男       广东  \n9996  1.520000e+18   25   女       广东  \n9997  1.520000e+18   23   男       浙江  \n9998  1.520000e+18   29   男       天津  \n9999  1.520000e+18   29   男       天津  \n\n[10000 rows x 12 columns]",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>id</th>\n      <th>start_time</th>\n      <th>oid</th>\n      <th>pid</th>\n      <th>type_id</th>\n      <th>type</th>\n      <th>brand</th>\n      <th>price</th>\n      <th>uid</th>\n      <th>age</th>\n      <th>sex</th>\n      <th>province</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>2020-04-24 11:50:39 UTC</td>\n      <td>2.294360e+18</td>\n      <td>1.515970e+18</td>\n      <td>2.270000e+18</td>\n      <td>electronics.tablet</td>\n      <td>samsung</td>\n      <td>162.01</td>\n      <td>1.520000e+18</td>\n      <td>24</td>\n      <td>女</td>\n      <td>海南</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>2020-04-24 11:50:39 UTC</td>\n      <td>2.294360e+18</td>\n      <td>1.515970e+18</td>\n      <td>2.270000e+18</td>\n      <td>electronics.tablet</td>\n      <td>samsung</td>\n      <td>162.01</td>\n      <td>1.520000e+18</td>\n      <td>24</td>\n      <td>女</td>\n      <td>海南</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2</td>\n      <td>2020-04-24 14:37:43 UTC</td>\n      <td>2.294440e+18</td>\n      <td>2.273950e+18</td>\n      <td>2.270000e+18</td>\n      <td>electronics.audio.headphone</td>\n      <td>huawei</td>\n      <td>77.52</td>\n      <td>1.520000e+18</td>\n      <td>38</td>\n      <td>女</td>\n      <td>北京</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>3</td>\n      <td>2020-04-24 14:37:43 UTC</td>\n      <td>2.294440e+18</td>\n      <td>2.273950e+18</td>\n      <td>2.270000e+18</td>\n      <td>electronics.audio.headphone</td>\n      <td>huawei</td>\n      <td>77.52</td>\n      <td>1.520000e+18</td>\n      <td>38</td>\n      <td>女</td>\n      <td>北京</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>4</td>\n      <td>2020-04-24 19:16:21 UTC</td>\n      <td>2.294580e+18</td>\n      <td>2.273950e+18</td>\n      <td>2.270000e+18</td>\n      <td>NaN</td>\n      <td>karcher</td>\n      <td>217.57</td>\n      <td>1.520000e+18</td>\n      <td>32</td>\n      <td>女</td>\n      <td>广东</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>9995</th>\n      <td>10001</td>\n      <td>2020-05-17 08:21:59 UTC</td>\n      <td>2.310920e+18</td>\n      <td>2.273950e+18</td>\n      <td>2.270000e+18</td>\n      <td>appliances.environment.fan</td>\n      <td>polaris</td>\n      <td>20.81</td>\n      <td>1.520000e+18</td>\n      <td>24</td>\n      <td>男</td>\n      <td>广东</td>\n    </tr>\n    <tr>\n      <th>9996</th>\n      <td>10002</td>\n      <td>2020-05-17 08:23:45 UTC</td>\n      <td>2.310930e+18</td>\n      <td>2.273950e+18</td>\n      <td>2.370000e+18</td>\n      <td>electronics.video.tv</td>\n      <td>samsung</td>\n      <td>416.64</td>\n      <td>1.520000e+18</td>\n      <td>25</td>\n      <td>女</td>\n      <td>广东</td>\n    </tr>\n    <tr>\n      <th>9997</th>\n      <td>10003</td>\n      <td>2020-05-17 08:24:07 UTC</td>\n      <td>2.310930e+18</td>\n      <td>1.515970e+18</td>\n      <td>2.270000e+18</td>\n      <td>computers.notebook</td>\n      <td>asus</td>\n      <td>509.24</td>\n      <td>1.520000e+18</td>\n      <td>23</td>\n      <td>男</td>\n      <td>浙江</td>\n    </tr>\n    <tr>\n      <th>9998</th>\n      <td>10004</td>\n      <td>2020-05-17 08:28:58 UTC</td>\n      <td>2.310930e+18</td>\n      <td>1.515970e+18</td>\n      <td>2.270000e+18</td>\n      <td>NaN</td>\n      <td>coolinar</td>\n      <td>3.22</td>\n      <td>1.520000e+18</td>\n      <td>29</td>\n      <td>男</td>\n      <td>天津</td>\n    </tr>\n    <tr>\n      <th>9999</th>\n      <td>10005</td>\n      <td>2020-05-17 08:28:58 UTC</td>\n      <td>2.310930e+18</td>\n      <td>1.515970e+18</td>\n      <td>2.270000e+18</td>\n      <td>NaN</td>\n      <td>coolinar</td>\n      <td>3.22</td>\n      <td>1.520000e+18</td>\n      <td>29</td>\n      <td>男</td>\n      <td>天津</td>\n    </tr>\n  </tbody>\n</table>\n<p>10000 rows × 12 columns</p>\n</div>"
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 = pd.read_csv(\"C:\\\\Users\\\\Administrator\\\\Desktop\\\\Python数据分析\\\\月考题\\\\大数据 大数据系 专高5 《Python数据分析EDA》月考题库（修改2）\\\\3-2 大数据 大数据系 专高5 《Python数据分析EDA》月考题库（新建）-已入库\\\\3-2 大数据 大数据系 专高5 《Python数据分析EDA》月考题库（新建）-已入库\\\\02-02-5-00006\\\\02-02-5-00006\\\\数据源\\\\手机销售分布分析.csv\"\n",
    "\t\t\t\t  ,encoding=\"GBK\")\n",
    "df1 = df1[:10000]\n",
    "df1"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 10000 entries, 0 to 9999\n",
      "Data columns (total 12 columns):\n",
      " #   Column      Non-Null Count  Dtype  \n",
      "---  ------      --------------  -----  \n",
      " 0   id          10000 non-null  int64  \n",
      " 1   start_time  10000 non-null  object \n",
      " 2   oid         10000 non-null  float64\n",
      " 3   pid         10000 non-null  float64\n",
      " 4   type_id     10000 non-null  float64\n",
      " 5   type        8002 non-null   object \n",
      " 6   brand       9640 non-null   object \n",
      " 7   price       10000 non-null  float64\n",
      " 8   uid         10000 non-null  float64\n",
      " 9   age         10000 non-null  int64  \n",
      " 10  sex         10000 non-null  object \n",
      " 11  province    10000 non-null  object \n",
      "dtypes: float64(5), int64(2), object(5)\n",
      "memory usage: 937.6+ KB\n"
     ]
    }
   ],
   "source": [
    "df1.info()\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "outputs": [
    {
     "data": {
      "text/plain": "         id               start_time           oid           pid  \\\n0         0  2020-04-24 11:50:39 UTC  2.294360e+18  1.515970e+18   \n1         1  2020-04-24 11:50:39 UTC  2.294360e+18  1.515970e+18   \n2         2  2020-04-24 14:37:43 UTC  2.294440e+18  2.273950e+18   \n3         3  2020-04-24 14:37:43 UTC  2.294440e+18  2.273950e+18   \n5         5  2020-04-26 08:45:57 UTC  2.295720e+18  1.515970e+18   \n...     ...                      ...           ...           ...   \n9993   9999  2020-05-17 08:20:21 UTC  2.310920e+18  1.515970e+18   \n9994  10000  2020-05-17 08:21:05 UTC  2.310920e+18  1.515970e+18   \n9995  10001  2020-05-17 08:21:59 UTC  2.310920e+18  2.273950e+18   \n9996  10002  2020-05-17 08:23:45 UTC  2.310930e+18  2.273950e+18   \n9997  10003  2020-05-17 08:24:07 UTC  2.310930e+18  1.515970e+18   \n\n           type_id                           type    brand   price  \\\n0     2.270000e+18             electronics.tablet  samsung  162.01   \n1     2.270000e+18             electronics.tablet  samsung  162.01   \n2     2.270000e+18    electronics.audio.headphone   huawei   77.52   \n3     2.270000e+18    electronics.audio.headphone   huawei   77.52   \n5     2.270000e+18        furniture.kitchen.table  maestro   39.33   \n...            ...                            ...      ...     ...   \n9993  2.270000e+18             computers.notebook   lenovo  370.35   \n9994  2.270000e+18  appliances.environment.vacuum   arnica   60.86   \n9995  2.270000e+18     appliances.environment.fan  polaris   20.81   \n9996  2.370000e+18           electronics.video.tv  samsung  416.64   \n9997  2.270000e+18             computers.notebook     asus  509.24   \n\n               uid  age sex province  \n0     1.520000e+18   24   女       海南  \n1     1.520000e+18   24   女       海南  \n2     1.520000e+18   38   女       北京  \n3     1.520000e+18   38   女       北京  \n5     1.520000e+18   20   男       重庆  \n...            ...  ...  ..      ...  \n9993  1.520000e+18   31   男       江苏  \n9994  1.520000e+18   19   女       上海  \n9995  1.520000e+18   24   男       广东  \n9996  1.520000e+18   25   女       广东  \n9997  1.520000e+18   23   男       浙江  \n\n[7757 rows x 12 columns]",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>id</th>\n      <th>start_time</th>\n      <th>oid</th>\n      <th>pid</th>\n      <th>type_id</th>\n      <th>type</th>\n      <th>brand</th>\n      <th>price</th>\n      <th>uid</th>\n      <th>age</th>\n      <th>sex</th>\n      <th>province</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>2020-04-24 11:50:39 UTC</td>\n      <td>2.294360e+18</td>\n      <td>1.515970e+18</td>\n      <td>2.270000e+18</td>\n      <td>electronics.tablet</td>\n      <td>samsung</td>\n      <td>162.01</td>\n      <td>1.520000e+18</td>\n      <td>24</td>\n      <td>女</td>\n      <td>海南</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>2020-04-24 11:50:39 UTC</td>\n      <td>2.294360e+18</td>\n      <td>1.515970e+18</td>\n      <td>2.270000e+18</td>\n      <td>electronics.tablet</td>\n      <td>samsung</td>\n      <td>162.01</td>\n      <td>1.520000e+18</td>\n      <td>24</td>\n      <td>女</td>\n      <td>海南</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2</td>\n      <td>2020-04-24 14:37:43 UTC</td>\n      <td>2.294440e+18</td>\n      <td>2.273950e+18</td>\n      <td>2.270000e+18</td>\n      <td>electronics.audio.headphone</td>\n      <td>huawei</td>\n      <td>77.52</td>\n      <td>1.520000e+18</td>\n      <td>38</td>\n      <td>女</td>\n      <td>北京</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>3</td>\n      <td>2020-04-24 14:37:43 UTC</td>\n      <td>2.294440e+18</td>\n      <td>2.273950e+18</td>\n      <td>2.270000e+18</td>\n      <td>electronics.audio.headphone</td>\n      <td>huawei</td>\n      <td>77.52</td>\n      <td>1.520000e+18</td>\n      <td>38</td>\n      <td>女</td>\n      <td>北京</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>5</td>\n      <td>2020-04-26 08:45:57 UTC</td>\n      <td>2.295720e+18</td>\n      <td>1.515970e+18</td>\n      <td>2.270000e+18</td>\n      <td>furniture.kitchen.table</td>\n      <td>maestro</td>\n      <td>39.33</td>\n      <td>1.520000e+18</td>\n      <td>20</td>\n      <td>男</td>\n      <td>重庆</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>9993</th>\n      <td>9999</td>\n      <td>2020-05-17 08:20:21 UTC</td>\n      <td>2.310920e+18</td>\n      <td>1.515970e+18</td>\n      <td>2.270000e+18</td>\n      <td>computers.notebook</td>\n      <td>lenovo</td>\n      <td>370.35</td>\n      <td>1.520000e+18</td>\n      <td>31</td>\n      <td>男</td>\n      <td>江苏</td>\n    </tr>\n    <tr>\n      <th>9994</th>\n      <td>10000</td>\n      <td>2020-05-17 08:21:05 UTC</td>\n      <td>2.310920e+18</td>\n      <td>1.515970e+18</td>\n      <td>2.270000e+18</td>\n      <td>appliances.environment.vacuum</td>\n      <td>arnica</td>\n      <td>60.86</td>\n      <td>1.520000e+18</td>\n      <td>19</td>\n      <td>女</td>\n      <td>上海</td>\n    </tr>\n    <tr>\n      <th>9995</th>\n      <td>10001</td>\n      <td>2020-05-17 08:21:59 UTC</td>\n      <td>2.310920e+18</td>\n      <td>2.273950e+18</td>\n      <td>2.270000e+18</td>\n      <td>appliances.environment.fan</td>\n      <td>polaris</td>\n      <td>20.81</td>\n      <td>1.520000e+18</td>\n      <td>24</td>\n      <td>男</td>\n      <td>广东</td>\n    </tr>\n    <tr>\n      <th>9996</th>\n      <td>10002</td>\n      <td>2020-05-17 08:23:45 UTC</td>\n      <td>2.310930e+18</td>\n      <td>2.273950e+18</td>\n      <td>2.370000e+18</td>\n      <td>electronics.video.tv</td>\n      <td>samsung</td>\n      <td>416.64</td>\n      <td>1.520000e+18</td>\n      <td>25</td>\n      <td>女</td>\n      <td>广东</td>\n    </tr>\n    <tr>\n      <th>9997</th>\n      <td>10003</td>\n      <td>2020-05-17 08:24:07 UTC</td>\n      <td>2.310930e+18</td>\n      <td>1.515970e+18</td>\n      <td>2.270000e+18</td>\n      <td>computers.notebook</td>\n      <td>asus</td>\n      <td>509.24</td>\n      <td>1.520000e+18</td>\n      <td>23</td>\n      <td>男</td>\n      <td>浙江</td>\n    </tr>\n  </tbody>\n</table>\n<p>7757 rows × 12 columns</p>\n</div>"
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.dropna(inplace=True)\n",
    "df1"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "outputs": [
    {
     "data": {
      "text/plain": "                 id           oid           pid       type_id        price  \\\ncount   7757.000000  7.757000e+03  7.757000e+03  7.757000e+03  7757.000000   \nmean    5060.945855  2.304480e+18  1.655729e+18  2.274624e+18   182.510990   \nstd     2868.013027  3.628350e+15  2.939943e+17  2.041798e+16   268.482744   \nmin        0.000000  2.294360e+18  1.515970e+18  2.270000e+18     0.020000   \n25%     2597.000000  2.301470e+18  1.515970e+18  2.270000e+18    30.070000   \n50%     5097.000000  2.304560e+18  1.515970e+18  2.270000e+18    94.880000   \n75%     7546.000000  2.307410e+18  1.515970e+18  2.270000e+18   231.460000   \nmax    10003.000000  2.310930e+18  2.298440e+18  2.370000e+18  9606.480000   \n\n                uid          age  \ncount  7.757000e+03  7757.000000  \nmean   1.520000e+18    32.926002  \nstd    0.000000e+00     9.941652  \nmin    1.520000e+18    16.000000  \n25%    1.520000e+18    24.000000  \n50%    1.520000e+18    33.000000  \n75%    1.520000e+18    41.000000  \nmax    1.520000e+18    50.000000  ",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>id</th>\n      <th>oid</th>\n      <th>pid</th>\n      <th>type_id</th>\n      <th>price</th>\n      <th>uid</th>\n      <th>age</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>7757.000000</td>\n      <td>7.757000e+03</td>\n      <td>7.757000e+03</td>\n      <td>7.757000e+03</td>\n      <td>7757.000000</td>\n      <td>7.757000e+03</td>\n      <td>7757.000000</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>5060.945855</td>\n      <td>2.304480e+18</td>\n      <td>1.655729e+18</td>\n      <td>2.274624e+18</td>\n      <td>182.510990</td>\n      <td>1.520000e+18</td>\n      <td>32.926002</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>2868.013027</td>\n      <td>3.628350e+15</td>\n      <td>2.939943e+17</td>\n      <td>2.041798e+16</td>\n      <td>268.482744</td>\n      <td>0.000000e+00</td>\n      <td>9.941652</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>0.000000</td>\n      <td>2.294360e+18</td>\n      <td>1.515970e+18</td>\n      <td>2.270000e+18</td>\n      <td>0.020000</td>\n      <td>1.520000e+18</td>\n      <td>16.000000</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>2597.000000</td>\n      <td>2.301470e+18</td>\n      <td>1.515970e+18</td>\n      <td>2.270000e+18</td>\n      <td>30.070000</td>\n      <td>1.520000e+18</td>\n      <td>24.000000</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>5097.000000</td>\n      <td>2.304560e+18</td>\n      <td>1.515970e+18</td>\n      <td>2.270000e+18</td>\n      <td>94.880000</td>\n      <td>1.520000e+18</td>\n      <td>33.000000</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>7546.000000</td>\n      <td>2.307410e+18</td>\n      <td>1.515970e+18</td>\n      <td>2.270000e+18</td>\n      <td>231.460000</td>\n      <td>1.520000e+18</td>\n      <td>41.000000</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>10003.000000</td>\n      <td>2.310930e+18</td>\n      <td>2.298440e+18</td>\n      <td>2.370000e+18</td>\n      <td>9606.480000</td>\n      <td>1.520000e+18</td>\n      <td>50.000000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.describe()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "outputs": [],
   "source": [
    "df1.fillna(0,inplace=True)\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "outputs": [],
   "source": [
    "df1.drop_duplicates(inplace=True)\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "outputs": [
    {
     "data": {
      "text/plain": "province  sex\n上海        女      107506.37\n          男      119127.97\n北京        女      114348.00\n          男      139256.82\n四川        女       32996.67\n          男       33581.58\n天津        女       34228.59\n          男       33894.09\n广东        女      155939.16\n          男      141757.78\n江苏        女       58287.90\n          男       42632.42\n浙江        女       39976.87\n          男       36975.09\n海南        女       42852.97\n          男       33662.30\n湖北        女       49565.34\n          男       42289.66\n湖南        女       49765.34\n          男       37416.76\n重庆        女       37842.62\n          男       31833.45\nName: price, dtype: float64"
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result = df1.groupby([\"province\",\"sex\"])[\"price\"].sum()\n",
    "result"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#\n",
    "# result.values\n",
    "# for index,x in enumerate(result.index.values):\n",
    "# \tplt.plot(str(x),result.values[index])"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = [str(i)for i in result.index.values]\n",
    "plt.plot(x,result.values)\n",
    "plt.xticks(rotation=-45)\n",
    "plt.title(\"不同性别和省份消费折线图\")\n",
    "plt.show()\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "outputs": [],
   "source": [
    "buy_count = df1[\"brand\"].value_counts()\n",
    "buy_money = df1.groupby(\"brand\")[\"price\"].sum()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "outputs": [
    {
     "data": {
      "text/plain": "<BarContainer object of 10 artists>"
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar(buy_count[:10].index,buy_count[:10].values,color=\"red\")\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "outputs": [
    {
     "data": {
      "text/plain": "<BarContainer object of 10 artists>"
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar(buy_money[:10].index,buy_money[:10].values,color=\"red\")\n",
    "\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "outputs": [],
   "source": [
    "df1[\"start_time\"] = pd.to_datetime(df1[\"start_time\"])\n",
    "df1[\"weekday\"] = df1[\"start_time\"].dt.weekday+1"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "outputs": [
    {
     "data": {
      "text/plain": "weekday\n0    173271.98\n1    139998.56\n2    179505.40\n3    265631.89\n4    240679.77\n5    242984.07\n6    173666.08\nName: price, dtype: float64"
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result1 = df1.groupby(\"weekday\")[\"price\"].sum()\n",
    "plt.hist(result1)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "result2 = df1[\"sex\"].value_counts()\n",
    "plt.pie(result2,labels=[\"女\",\"男\"],autopct=\"%.1f%%\")\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "outputs": [],
   "source": [
    "bins=[0,30,60,100]\n",
    "lables=['年轻人','青年人','老年人']\n",
    "df1[\"age_box\"] = pd.cut(df1[\"age\"],bins=bins,labels=lables)\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "outputs": [
    {
     "data": {
      "text/plain": "age_box  sex\n年轻人      女      1711\n         男      1596\n青年人      女      2255\n         男      2195\n老年人      女         0\n         男         0\nName: id, dtype: int64"
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result3 = df1.groupby([\"age_box\",\"sex\"])[\"id\"].count()\n",
    "result3"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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c3Fz/98XFxaqqqlJcXFyDY5s3b1a3bt1Oe7mTjwEAADQ6UoYOHSqfz6elS5dKkrKysjRq1CiFh4crPT1dmzZt0po1a1RdXa25c+dqzJgxkqSJEyfq+eef19atW3Xw4EEtXLjQfwwAAKDRD/d4vV4tXrxYGRkZyszMVFhYmDZs2CBJ6tSpk+bPn6+xY8eqQ4cOiomJ8b+HSv/+/XXHHXdo4MCBioyMVFJSkn7xi180djkAAKCF8NjJbw/bCHv37lV+fr5SU1MVHx9f71hxcbF27NihIUOGqEOHDvWOFRUV6csvv9SwYcO+9Tkpp/L5fIqOjlZFRYWioqKa4irU8/dXTbc6jd0NzC04zC1wzAxongK5/W6ySDnXiJSzgxvb4DC34BApgWuev7GBfwjk9psPGAQAAE4iUgAAgJOIFAAA4CQiBQAAOIlIAQAATiJSAACAk4gUAADgJCIFAAA4iUgBAABOIlIAAICTiBQAAOAkIgUAADiJSAEAAE4iUgAAgJOIFAAA4CQiBQAAOIlIAQAATiJSAACAk4gUAADgJCIFAAA4iUgBAABOIlIAAICTiBQAAOAkIgUAADiJSAEAAE4iUgAAgJOIFAAA4CQiBQAAOIlIAQAATiJSAACAk4gUAADgJCIFAAA4iUgBAABOIlIAAICTiBQAAOAkIgUAADiJSAEAAE4iUgAAgJOIFAAA4CQiBQAAOIlIAQAATiJSAACAk4gUAADgJCIFAAA4iUgBAABOIlIAAICTiBQAAOAkIgUAADiJSAEAAE4iUgAAgJOIFAAA4CQiBQAAOIlIAQAATiJSAACAk4gUAADgJCIFAAA4iUgBAABOIlIAAICTiBQAAOAkIgUAADiJSAEAAE4iUgAAgJOIFAAA4CQiBQAAOIlIAQAATiJSAACAk5osUmbMmCGPx+P/6tmzpySpsLBQKSkpio2NVWZmpszMf5mNGzeqT58+6tSpk+bNm9dUSwEAAC1Ak0VKXl6eVq5cqfLycpWXl2vz5s2qqqpSWlqaBgwYoLy8PBUVFSknJ0eSVFpaqvT0dGVkZCg3N1fLly/X+vXrm2o5AACgmWuSSKmpqdG2bds0dOhQxcTEKCYmRh07dtSqVatUUVGhefPmqUePHsrKytLTTz8tSVq+fLm6du2qWbNmKSkpSbNnz/YfAwAAaJJI2bp1q+rq6pScnKx27drp6quv1u7du1VQUKDU1FS1b99ektSvXz8VFRVJkgoKCjRixAh5PB5J0qBBg5Sfn/+Nf0dVVZV8Pl+9LwAA0HI1SaQUFRWpd+/eWrZsmbZs2SKv16vp06fL5/MpMTHRfz6Px6Pw8HCVl5c3OBYVFaWSkpJv/Duys7MVHR3t/0pISGiKpQMAAEc1SaRMmTJFeXl5uuKKK5SUlKTHHntMf/3rX1VXV6eIiIh6542MjNThw4fl9XrrHTtx+jeZOXOmKioq/F979uxpiqUDAABHec/GD+3SpYvq6up0wQUXqLCwsN6xyspKtW3bVnFxcSotLW1w+jeJiIhoEDwAAKDlapJ7UjIzM/Xss8/6v8/NzVVYWJguvfRS5ebm+k8vLi5WVVWV4uLilJKSUu/Y5s2b1a1bt6ZYDgAAaAGaJFL69++ve+65R2vXrtXq1at1yy236Gc/+5lGjx4tn8+npUuXSpKysrI0atQohYeHKz09XZs2bdKaNWtUXV2tuXPnasyYMU2xHAAA0AI0ycM9U6dO1bZt2zRx4kSFh4dr6tSpysrKktfr1eLFi5WRkaHMzEyFhYVpw4YNkqROnTpp/vz5Gjt2rDp06KCYmBj/e6gAAAB47OS3gD1L9u7dq/z8fKWmpio+Pr7eseLiYu3YsUNDhgxRhw4dzvhn+nw+RUdHq6KiQlFRUU29ZP39ldGtTmN3A3MLDnMLHDMDmqdAbr/PSaScDUTK2cGNbXCYW3CIlMA1z9/YwD8EcvvNBwwCAAAnESkAAMBJRAoAAHASkQIAAJxEpAAAACcRKQAAwElECgAAcBKRAgAAnESkAAAAJxEpAADASUQKAABwEpECAACcRKQAAAAnESkAAMBJRAoAAHASkQIAAJxEpAAAACcRKQAAwElECgAAcBKRAgAAnESkAAAAJxEpAADASUQKAABwEpECAACcRKQAAAAnESkAAMBJRAoAAHASkQIAAJxEpAAAACcRKQAAwElECgAAcBKRAgAAnESkAAAAJxEpAADASUQKAABwEpECAACcRKQAAAAnESkAAMBJRAoAAHASkQIAAJxEpAAAACcRKQAAwElECgAAcBKRAgAAnESkAAAAJxEpAADASUQKAABwEpECAACcRKQAAAAnESkAAMBJRAoAAHASkQIAAJxEpAAAACcRKQAAwElECgAAcBKRAgAAnESkAAAAJxEpAADASUQKAABwEpECAACcRKQAAAAnESkAAMBJRAoAAHASkQIAAJxEpAAAACcRKQAAwEkhj5TCwkKlpKQoNjZWmZmZMrNQLwkAADggpJFSVVWltLQ0DRgwQHl5eSoqKlJOTk4olwQAABwR0khZtWqVKioqNG/ePPXo0UNZWVl6+umnQ7kkAADgCG8o//KCggKlpqaqffv2kqR+/fqpqKjotOetqqpSVVWV//uKigpJks/nO/sLbUUYZ3CYW3CYW+CYGZq7E7fbZ/L0jpBGis/nU2Jiov97j8ej8PBwlZeXKzY2tt55s7Oz9Zvf/KbBz0hISDjr62xNoqNDvYLmibkFh7kFjpmhpaisrFT0d2zokEaK1+tVREREvdMiIyN1+PDhBpEyc+ZM3XXXXf7v6+rqdODAAcXHx8vj8ZyT9Z4LPp9PCQkJ2rNnj6KiokK9nGaDuQWHuQWOmQWHuQWnJc7NzFRZWamuXbt+53lDGilxcXEqLCysd1plZaXatm3b4LwRERENgiYmJuZsLi+koqKiWsyGPJeYW3CYW+CYWXCYW3Ba2ty+6x6UE0L6xNmUlBTl5ub6vy8uLlZVVZXi4uJCuCoAAOCCkEbK0KFD5fP5tHTpUklSVlaWRo0apfDw8FAuCwAAOCDkz0lZvHixMjIylJmZqbCwMG3YsCGUSwq5iIgI3XvvvQ0e2sK3Y27BYW6BY2bBYW7Bae1z85gDb/G6d+9e5efnKzU1VfHx8aFeDgAAcIATkQIAAHCqkH92DwAAwOkQKQAAwElECgAAcBKRchadeLoPT/sJDHMLTk1NjcrLy3Xs2LFQL6XZYGaA24iUs+DEjeuJt+v3+XzavXu3Xn/99VAuy3nMrXF8Pp9Wr16tbdu26ejRo6FeTrPAzBqP/0wEjpmdOSLlLNi3b59qa2v1yiuv6NFHH9W4ceP0ox/9SEuWLNGuXbtCvTxnMbfgbNmyRT6fTxs3btSjjz6qjz76SJ9++qlqa2tDvTRnMbPGWbVqlRYsWCDp+H8q9u/fH+IVuY+ZBYeXIDcxn8+nG2+8UW+//bbi4+PVo0cPXXTRRVq1apV27typNm3ahHqJTmJuwamrq9PgwYNVW1urV199Vddff72qq6v17//+7+rUqZPatWunH/7wh6FeplOYWXDeeecdffXVV7r22mtVVlam1NRUTZ06VTfddJMeeughTZkyRZdffnmol+kUZtZ4REoTqaurU1hYmA4dOqTy8nKtXr1aN910k44dO6a2bdtqzpw5euutt/Tyyy+rY8eO/vO3dsyt8Q4ePKhRo0apc+fOevzxx3XzzTfrwIED6tSpk44cOaInnnhCSUlJoV6mU5hZ4NavX6/09HQtW7ZMEyZMUGVlpS6//HJ17txZP/jBD/Qv//Iv+vGPfxzqZTqFmTUev+2bQFVVlRYtWqRPPvlE5513nj744AP95je/0fr16/2f6DxnzhwdOXJEGRkZOnTokMLCwlRXVxfilYcWc2u8L7/8Uq+//rrWrVun8vJyzZgxQ/PmzVNZWZk+/PBDJSUl6dixY6qurg71Up3BzIIzYsQIvfzyy7rrrru0cuVKRUREaNSoUdq2bZvq6ur8N7b8+/wHZtZ4REoTiIiIkJnpJz/5iZ555hllZWVp/Pjxuv3227V27VpJ0q9//WtFREQoOjpa6enpOnjwYKu/wWVujfP1119r+fLlmjx5sjIyMrR69Wr93//9n4YPH66BAwcqLi5OhYWF2rFjhz7++GOeGCpmFqi1a9dqypQp+stf/qKysjJdeeWVWrJkiWbMmKHJkyerXbt2Wrx4sd59913dfffdkuS/p7O13knPzJqYock8/vjjdvHFF1tWVpaZmb399tvWv39/u/7662348OG2du1aMzObM2eODR482Hw+XyiX6wzmFriKigrLzs62/v372+9+9zsbO3asXX/99Xbw4EEbP3689e7d20pKSmzMmDE2aNAgW7ZsmeXl5dmxY8dCvfSQYWaBO3r0qN188812xx13WK9evWzy5Mn27LPP2sKFC61r1672xBNPmJnZgQMHbNq0abZo0SJ78skn7auvvgrxykOHmTUtIqUJ1NbW+v/85JNP2sCBA23fvn1mZpaRkWHnnXeePfDAA/Uu86tf/cqmTZtm1dXVZmZWU1Nz7hbsCOYWnK+++srmzp1rcXFx1rdvX9u0aZOtXr3ahg0bZpMmTbJDhw7Zj370I5swYYLt2bPHhg0bZmlpabZy5Uo7cuRIqJcfEswscKf+27rrrrvsiiuusIEDB9p//Md/WOfOnc3r9VrPnj1t2rRplpqaaj179rTx48fbsmXLQrTq0GJmTY9IaSJ1dXX+Py9atMiuuOIKu/XWW2306NH24IMP2iWXXGKrVq2qd5n33nvP8vPz/b8EDx8+fE7X7ALmFrgVK1bYwIED7f3337cJEybYkCFD7NVXX7U1a9bY0KFD7frrr7dDhw7Z4MGDLS0tzfbt22dbtmzxh93JM28tmFnwamtr7Q9/+IOlpaXZNddcY//5n/9pX3/9tR05csTy8vKsR48edu+995qZ2ddff93gsq0RM2s6REoTqqqq8v/5qquuso4dO/rreMOGDZaUlGR/+ctf6l3mySeftG7dutnKlSvtn/7pn+y11147p2t2AXML3GeffWZmx+9aHjNmTL0b3auvvtrGjx9vhw4dsssvv9z69OljlZWV9S5fW1trZWVl9t5774Vi+SHBzIJTWVlpkydPtlmzZllubq7dd999lpycbI888oitW7fO8vLyLDEx0V5//XUza3gj2xrnxsyaDpFyFsycOdNGjhxpv/jFL2zgwIH2xRdfmJnZmjVr7Jlnnql33q1bt1qvXr0sMjLSbr/9djNrnQ9hmDG3M3Hy/+hPPFeiurraRo8ebUOHDrWNGzfa3LlzrX379paTk2MVFRU2depUKy0tNbPj90KtWrXK/vmf/9lSU1MtOTnZtm/fHpLrcq4ws8a77rrrbOTIkf7vX3rpJbv//vutd+/e9sEHH9jatWstMjLS3nrrLf+9T619bsysaRApTWz58uXWtWtXe/PNN83M7NFHH7VBgwZZcXFxvfP99a9/tYcfftjOP/98W7Bggd11112WnJzsL+jWhrkF58S9UFVVVTZx4kRLSkqyUaNGWXZ2thUUFPjPd/fdd9v3v/99O++88ywtLc1GjBhhXbp08c+3NT2cwczO3MnX8brrrrOxY8fWO75//37/v7u8vDw7fPiw3X777darV69WOzdm1rSIlLPggw8+sIMHD/q/X7BggfXs2dM+//xzMzO78cYbrWfPnnbdddfZSy+9ZC+//LKZmU2fPt1++9vfWnp6ui1ZsiQkaw8l5hacmpoay8nJsZycHOvevbtlZ2f7n4BsdvxVBB988IE988wztnfvXv89Tvfee6+NHj3a/5h4a7gn6gRmduZO3EDW1tbapEmTLC0tzUpLS/0Pn5n9I/wOHTpk7733XqufGzNrOkRKEzrd44on/P73v7e3337bzMxmz55tV1xxhWVnZ1tmZqZNmDCh3vMy1q9fb7GxsbZ06dJzsu5QY26NN2nSJLv00ktt7ty5tmvXLqurq7OlS5faRx99ZGbHHyMfMGCArV69ut7lrr/+ektLS/PHYWt60h4zO3Mn/09+0qRJdtlll9kPfvAD+/TTT+3IkSO2ZMkS++STT8yMuZ3AzJoGkXKWndhUx44ds0ceecR/+gMPPGC9evWylJQU+/TTTxtcLi8vz+Lj4+2ll146V0t1CnM7Myf/Ity5c2e9l8s+9dRT1qdPHysqKrLk5GQbMGCAJScn+59kvHLlSouPj7err77aRo4c6X+iaEv/RcjMgnPyddy+fbu9//77/u8XL17M3E6DmTUekXKO7Nixw84//3ybPHmyvfvuu5aZmWmXXXaZDR8+3LKysuyxxx6zP/7xj/bcc89Zdna2Pfzww+bxeCw1NdV27NgR6uWHDHP7bt/2ePWCBQvs4osvtgkTJpjZ8XubLrvsMrv33nstLi7OHn/8cTMzmzVrlg0bNqzVvFEeMwsOcwscM2scIuUcqqmpsRtuuMF++MMfWs+ePa2kpMS++OILe/nlly0xMdGSkpJs6NCh5vF47Kc//amtWLHC9u3b1+rK+VTMLXAnX/ecnBwbNGiQ7d6928zM7r//fgsPD7fbbrut3mVmzJhh11xzjf+VBq0NMwsOcwscMztz3lC/LX9rUVNTowMHDig+Pl6xsbG6/PLLVVhYqP3792vPnj367LPP/J/wO23aNMXFxSk9Pd1/eTOTx+MJ4TUIDeYWnLCwMP91v/HGG1VeXq6MjAz9/Oc/1/z58/XLX/5S69at08qVKzVu3DhJ0oIFC/TOO+/o8OHDioqKCvE1OPeYWXCYW+CYWQBCWUitzRNPPGEjRoyw/fv3m9nx51t89tlnlpiYaAsXLvSf78Ybb7Rx48aFapnOYW7BO/kVAf/6r/9qXq/X7r//fjM7/v4zvXv3tldeeSVUy3MSMwsOcwscM/tufAryOTR9+nStXr1aa9eu1ZEjR9SmTRslJiZq2rRpuuOOOzR//nxJ0qWXXiqv16vq6mo+FVPMrTHCw8MlSStXrtRLL72ka6+9Vq+++qp27dqlK6+8Ur/73e+0efPmEK/SLcwsOMwtcMzsDIS6klqLk588NXr0aBs8eLCZmS1dutQGDhxoy5cvt4svvtgWL15seXl59uqrr37jzzrxeGZreIMf5tZ4eXl55vF47LHHHjOz40/WS0lJ8b//zHe9/0JrnBszCw5zCxwz+3ZEyjl08iYaP368fe9737N+/frZu+++a2Zmu3btsiVLlnzrEz5PbNj9+/dbdnZ2gw+naomYW+Od+IyQExYuXGh9+vSxjz/++Fsv15rnxsyCw9wCx8y+mceM+8XPpRNP8pSOP4yRm5urrVu3nvb4qezvT7Q6dOiQ+vbtqwsuuEBvvPGGYmNjz8naQ4m5BefUudhJTyR++OGH9b3vfU8TJ0487WVb69yYWXCYW+CY2XcjUkLg5I05YcIE7dq16xsfd7RTXp2ye/dujRo1Sj169NBzzz2nmJiYc7FkJzC3pnEmQXcCczuOmQWHuQWOmdXHE2dDICwsTHV1dZKkl19+WZ07d9bSpUvrnae2tlaS/BuyuLhYzz33nH784x/rggsu0PPPP98iN+S3YW5N43S/AGtqaiQxt2/CzILD3ALHzOrjfVJC5MQNblhYmFavXq2jR4/qww8/VLt27RQbG6uOHTuqXbt2evvtt/XBBx9o2bJlatOmjWpra/Xwww8rOjo61FchJJhb0zl1bl6vl7l9B2YWHOYWOGZ2HJESQie/oU9kZKQeeughrVu3TmamNm3aKDIyUh6PRzExMbrvvvv0wgsvKD4+Xv379w/10kOKuTWN//7v/9aGDRtUV1fH3M4QMwsOcwscMzuO56Q4ZufOndq/f79qamoUHh6uvn37SpI6duyo0aNHKzMzU1ddddW3Pm7ZGjXF3Gpra/3vW9BasN8Cx14LDnstcOw17klxxol7Bnr06KEePXo0OP7xxx9r+/bt6ty5s6TTP2558s9pLZpqbpWVlXrhhRd00UUX6eqrrz6ra3YB+y1w7LXgsNcCx177B3LVEd/1j2/Lli1KSEhQcnLyad9N9fPPP1dJSYk8Ho9qampazTuuNnZuknT48GGtWrVKixcv1rhx4/T73//+bCzVKey3wLHXgsNeCxx77R+IlGbik08+0aFDhySdfgN/+eWXSk5OVl5enrxer/9VMEuXLlV+fv45XatLvmtuBw8e1CuvvKIHH3xQHTt21N69e/Xggw8qJyfnHK/ULey3wLHXgsNeC1xr2mtESjMRExOjDh06SJKqq6sbHB88eLBmzpypCRMmaPPmzXrhhRc0ceJE3XrrrSopKfG/NLe1+ba5HTlyRH/+85+Vk5Oj1NRU7du3T6+99pq2bNmiOXPm6MUXXwzFkp3Afgscey047LXAtaa9xnNSmon27dvr448/1v79+xUfH1/v2IknTd15553asmWLBg8erJSUFI0dO1bZ2dnq1atXiFYdeifmduDAAcXFxflP37Vrl7p3767XX39dbdq00aJFi1ReXq6RI0fqiy++0O7duzVt2jQdOXJEN954YwivQWiw3wLHXgsOey1wrWmvcU9KM9G5c2d17dpVX3zxRYNjR44c0dy5c3XNNddoz549GjJkiAoKCjRy5Ej16tVLx44dC8GK3dC5c2ddeOGF2rNnj/+0Q4cOaciQIZo/f74eeughlZaW6vXXX1dsbKwWLlyo++67T0OHDlVBQYGys7Ob7WO5jcF+Cxx7LTjstcC1qr12dj4SCGfD888/b/v37/d/X1dXZ7W1tbZq1SqLjY21N954w4qKiszM7LHHHrMuXbrYhx9+aGbf/UmaLdkLL7xgFRUV9U4rKiqyiy66yHJycmz//v2WnJxsS5YssQ4dOtgNN9xgffv2tSeffNIOHTpknTp1shdeeCFEqw8d9lvg2GvBYa8FrrXsNSKlGTj1I7j37dtX7/vS0lLr3r27Pf/88/VOX7hwoV1wwQW2Y8cOM2t9/5hPndupnzT6ySefWLdu3eyee+6xMWPGWO/evW3MmDFmZrZ371677LLL7M4777SrrrrKJk2aZAcOHDhnaw8l9lvg2GvBYa8FrrXtNSKlGVqwYIFdddVVlpub6/9H/dRTT1lmZqZVV1ebmdnOnTvtb3/7m3k8Hrvwwgv9/+uora0N2bpD6ejRo3bVVVfZZZddZu+88459+umnZmY2Z84ca9OmjV188cX24osvWmpqqq1YscLMzP785z9bly5d7NZbb7X//d//DeXyQ4r9Fhj2WvDYa4FpDXuNd5xtpm699VaVlZXJ6/Vq+vTp2rlzpxYuXKg5c+aooqJC8+bNU69evXTppZeqW7duuvPOO7V582YlJSWFeukh4/P5dPfdd6usrEwbNmxQYmKi9u7dq9tuu02PPvqo/ud//kcDBw7UT3/6U40bN067d++W1+vVv/3bvyk5OTnUyw8p9ltg2GvBY68FpqXvNSKlmTn57Y/ff/99bdq0Sc8884y6dOmi1atXq2/fvurevbuuvfZaDR8+XD179pQkPfvss2rTpo0mTZoUyuU74cCBAyooKJCZqU+fPrrwwgv1+eef64orrlBWVpauvPJK3XDDDerZs6duvfVWXX755aFecsiw3xqHvXbm2GuN01L3GpHSDNkpbw9dVlammpoaPfnkk3rjjTe0bt06RUZG+o/zWRjHnTq3E44eParIyEjt3LlTw4YN0wMPPKApU6bok08+UZ8+fUKwUrew3wLHXgsOey1wLX6vheAhJpwlPp/PLrnkEnvjjTfMrOETrNDQqTN65JFHzOPxWGFhYYhW1Hyw3wLDXgseey0wLWmvte4EbUHq6urk9Xp10UUXafv27ZK++/MfcPxzQR544AFt375dCxYs0BNPPKHZs2frkksuCfXSnMZ+Cxx7LTjstcC1pL1GpLQQYWFhateunUaMGKFly5bp66+/DvWSmoWjR4/qxRdf1PDhw7Vy5UrdddddmjNnTqiX5Tz2W+DYa8FhrwWuJe01npPSwqxYsUKlpaWaNm2avF4+9eBM/e1vf9P555+v3r17S2pdHwvfGOy3wLHXgsNeC1xL2GtEClq15viPFs0Tew3nSkvaa0QKAABwEs9JAQAATiJSAACAk4gUAADgJCIFAAA4iUgBAABOIlIAAICTiBQAAOAkIgUAADiJSAEAAE4iUgAAgJP+H8tHrX74j/h7AAAAAElFTkSuQmCC"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for index,x in enumerate(result3.index.values):\n",
    "\tplt.bar(\"\".join(x),result3.values[index],color=\"blue\",lw=2)\n",
    "plt.xticks(rotation=-45)\n",
    "plt.show()\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "outputs": [
    {
     "data": {
      "text/plain": "                      price\nage_box province           \n年轻人     上海         98056.43\n        北京        103678.32\n        四川         31239.18\n        天津         27509.69\n        广东        126219.92\n        江苏         48917.64\n        浙江         37262.57\n        海南         28973.57\n        湖北         45052.51\n        湖南         53781.39\n        重庆         26370.21\n青年人     上海        128577.91\n        北京        149926.50\n        四川         35339.07\n        天津         40612.99\n        广东        171477.02\n        江苏         52002.68\n        浙江         39689.39\n        海南         47541.70\n        湖北         46802.49\n        湖南         33400.71\n        重庆         43305.86\n老年人     上海             0.00\n        北京             0.00\n        四川             0.00\n        天津             0.00\n        广东             0.00\n        江苏             0.00\n        浙江             0.00\n        海南             0.00\n        湖北             0.00\n        湖南             0.00\n        重庆             0.00",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th></th>\n      <th>price</th>\n    </tr>\n    <tr>\n      <th>age_box</th>\n      <th>province</th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th rowspan=\"11\" valign=\"top\">年轻人</th>\n      <th>上海</th>\n      <td>98056.43</td>\n    </tr>\n    <tr>\n      <th>北京</th>\n      <td>103678.32</td>\n    </tr>\n    <tr>\n      <th>四川</th>\n      <td>31239.18</td>\n    </tr>\n    <tr>\n      <th>天津</th>\n      <td>27509.69</td>\n    </tr>\n    <tr>\n      <th>广东</th>\n      <td>126219.92</td>\n    </tr>\n    <tr>\n      <th>江苏</th>\n      <td>48917.64</td>\n    </tr>\n    <tr>\n      <th>浙江</th>\n      <td>37262.57</td>\n    </tr>\n    <tr>\n      <th>海南</th>\n      <td>28973.57</td>\n    </tr>\n    <tr>\n      <th>湖北</th>\n      <td>45052.51</td>\n    </tr>\n    <tr>\n      <th>湖南</th>\n      <td>53781.39</td>\n    </tr>\n    <tr>\n      <th>重庆</th>\n      <td>26370.21</td>\n    </tr>\n    <tr>\n      <th rowspan=\"11\" valign=\"top\">青年人</th>\n      <th>上海</th>\n      <td>128577.91</td>\n    </tr>\n    <tr>\n      <th>北京</th>\n      <td>149926.50</td>\n    </tr>\n    <tr>\n      <th>四川</th>\n      <td>35339.07</td>\n    </tr>\n    <tr>\n      <th>天津</th>\n      <td>40612.99</td>\n    </tr>\n    <tr>\n      <th>广东</th>\n      <td>171477.02</td>\n    </tr>\n    <tr>\n      <th>江苏</th>\n      <td>52002.68</td>\n    </tr>\n    <tr>\n      <th>浙江</th>\n      <td>39689.39</td>\n    </tr>\n    <tr>\n      <th>海南</th>\n      <td>47541.70</td>\n    </tr>\n    <tr>\n      <th>湖北</th>\n      <td>46802.49</td>\n    </tr>\n    <tr>\n      <th>湖南</th>\n      <td>33400.71</td>\n    </tr>\n    <tr>\n      <th>重庆</th>\n      <td>43305.86</td>\n    </tr>\n    <tr>\n      <th rowspan=\"11\" valign=\"top\">老年人</th>\n      <th>上海</th>\n      <td>0.00</td>\n    </tr>\n    <tr>\n      <th>北京</th>\n      <td>0.00</td>\n    </tr>\n    <tr>\n      <th>四川</th>\n      <td>0.00</td>\n    </tr>\n    <tr>\n      <th>天津</th>\n      <td>0.00</td>\n    </tr>\n    <tr>\n      <th>广东</th>\n      <td>0.00</td>\n    </tr>\n    <tr>\n      <th>江苏</th>\n      <td>0.00</td>\n    </tr>\n    <tr>\n      <th>浙江</th>\n      <td>0.00</td>\n    </tr>\n    <tr>\n      <th>海南</th>\n      <td>0.00</td>\n    </tr>\n    <tr>\n      <th>湖北</th>\n      <td>0.00</td>\n    </tr>\n    <tr>\n      <th>湖南</th>\n      <td>0.00</td>\n    </tr>\n    <tr>\n      <th>重庆</th>\n      <td>0.00</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 该城市人群购物车总额\n",
    "result5 = df1.groupby([\"age_box\",\"province\"])[[\"price\"]].sum()\n",
    "result5"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "outputs": [
    {
     "data": {
      "text/plain": "price    85244.02\nName: (年轻人, 上海, 1.51597e+18), dtype: float64"
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 年轻人 上海  手机  10000元\n",
    "# 年轻人 上海  20000\n",
    "# 手机在年轻人上海的渗透率50%\n",
    "result4 = df1.groupby([\"age_box\",\"province\",\"pid\"])[[\"price\"]].sum()\n",
    "result4.loc[('年轻人', '上海', 1.51597e+18)]"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "outputs": [
    {
     "data": {
      "text/plain": "array([98056.43])"
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "该1.51597e+18在年轻人上海渗透率为0.8693363607057691\n",
      "该2.27395e+18在年轻人上海渗透率为0.1268518545902599\n",
      "该2.29844e+18在年轻人上海渗透率为0.0038117847039709682\n",
      "该1.51597e+18在年轻人北京渗透率为0.9166997497644637\n",
      "该2.27395e+18在年轻人北京渗透率为0.08149225411831518\n",
      "该2.29844e+18在年轻人北京渗透率为0.001807996117221035\n",
      "该1.51597e+18在年轻人四川渗透率为0.9077238903197843\n",
      "该2.27395e+18在年轻人四川渗透率为0.08634893745610481\n",
      "该2.29844e+18在年轻人四川渗透率为0.005927172224110876\n",
      "该1.51597e+18在年轻人天津渗透率为0.8173559934699374\n",
      "该2.27395e+18在年轻人天津渗透率为0.1537829034060362\n",
      "该2.29844e+18在年轻人天津渗透率为0.028861103124026483\n",
      "该1.51597e+18在年轻人广东渗透率为0.8499585485397233\n",
      "该2.27395e+18在年轻人广东渗透率为0.15004145146027664\n",
      "该2.29844e+18在年轻人广东渗透率为0.0\n",
      "该1.51597e+18在年轻人江苏渗透率为0.8892358666526022\n",
      "该2.27395e+18在年轻人江苏渗透率为0.11076413334739778\n",
      "该2.29844e+18在年轻人江苏渗透率为0.0\n",
      "该1.51597e+18在年轻人浙江渗透率为0.8913703483146761\n",
      "该2.27395e+18在年轻人浙江渗透率为0.10862965168532392\n",
      "该2.29844e+18在年轻人浙江渗透率为0.0\n",
      "该1.51597e+18在年轻人海南渗透率为0.8425465001378843\n",
      "该2.27395e+18在年轻人海南渗透率为0.1574534998621157\n",
      "该2.29844e+18在年轻人海南渗透率为0.0\n",
      "该1.51597e+18在年轻人湖北渗透率为0.902323533139441\n",
      "该2.27395e+18在年轻人湖北渗透率为0.09767646686055892\n",
      "该2.29844e+18在年轻人湖北渗透率为0.0\n",
      "该1.51597e+18在年轻人湖南渗透率为0.8991954280095774\n",
      "该2.27395e+18在年轻人湖南渗透率为0.10080457199042271\n",
      "该2.29844e+18在年轻人湖南渗透率为0.0\n",
      "该1.51597e+18在年轻人重庆渗透率为0.8757761125148417\n",
      "该2.27395e+18在年轻人重庆渗透率为0.12422388748515845\n",
      "该2.29844e+18在年轻人重庆渗透率为0.0\n",
      "该1.51597e+18在青年人上海渗透率为0.8550814832812261\n",
      "该2.27395e+18在青年人上海渗透率为0.1434784559805024\n",
      "该2.29844e+18在青年人上海渗透率为0.0014400607382714496\n",
      "该1.51597e+18在青年人北京渗透率为0.8985254774839672\n",
      "该2.27395e+18在青年人北京渗透率为0.10147452251603285\n",
      "该2.29844e+18在青年人北京渗透率为0.0\n",
      "该1.51597e+18在青年人四川渗透率为0.922273562943224\n",
      "该2.27395e+18在青年人四川渗透率为0.07772643705677598\n",
      "该2.29844e+18在青年人四川渗透率为0.0\n",
      "该1.51597e+18在青年人天津渗透率为0.8657907236083825\n",
      "该2.27395e+18在青年人天津渗透率为0.13420927639161756\n",
      "该2.29844e+18在青年人天津渗透率为0.0\n",
      "该1.51597e+18在青年人广东渗透率为0.9070520353106206\n",
      "该2.27395e+18在青年人广东渗透率为0.09294796468937938\n",
      "该2.29844e+18在青年人广东渗透率为0.0\n",
      "该1.51597e+18在青年人江苏渗透率为0.9140027013992357\n",
      "该2.27395e+18在青年人江苏渗透率为0.08599729860076442\n",
      "该2.29844e+18在青年人江苏渗透率为0.0\n",
      "该1.51597e+18在青年人浙江渗透率为0.9176905969076371\n",
      "该2.27395e+18在青年人浙江渗透率为0.08230940309236297\n",
      "该2.29844e+18在青年人浙江渗透率为0.0\n",
      "该1.51597e+18在青年人海南渗透率为0.8911141166596904\n",
      "该2.27395e+18在青年人海南渗透率为0.10888588334030967\n",
      "该2.29844e+18在青年人海南渗透率为0.0\n",
      "该1.51597e+18在青年人湖北渗透率为0.8707899942930387\n",
      "该2.27395e+18在青年人湖北渗透率为0.12921000570696134\n",
      "该2.29844e+18在青年人湖北渗透率为0.0\n",
      "该1.51597e+18在青年人湖南渗透率为0.8979189364537461\n",
      "该2.27395e+18在青年人湖南渗透率为0.10208106354625396\n",
      "该2.29844e+18在青年人湖南渗透率为0.0\n",
      "该1.51597e+18在青年人重庆渗透率为0.8824976573609207\n",
      "该2.27395e+18在青年人重庆渗透率为0.11750234263907933\n",
      "该2.29844e+18在青年人重庆渗透率为0.0\n",
      "该1.51597e+18在老年人上海渗透率为nan\n",
      "该2.27395e+18在老年人上海渗透率为nan\n",
      "该2.29844e+18在老年人上海渗透率为nan\n",
      "该1.51597e+18在老年人北京渗透率为nan\n",
      "该2.27395e+18在老年人北京渗透率为nan\n",
      "该2.29844e+18在老年人北京渗透率为nan\n",
      "该1.51597e+18在老年人四川渗透率为nan\n",
      "该2.27395e+18在老年人四川渗透率为nan\n",
      "该2.29844e+18在老年人四川渗透率为nan\n",
      "该1.51597e+18在老年人天津渗透率为nan\n",
      "该2.27395e+18在老年人天津渗透率为nan\n",
      "该2.29844e+18在老年人天津渗透率为nan\n",
      "该1.51597e+18在老年人广东渗透率为nan\n",
      "该2.27395e+18在老年人广东渗透率为nan\n",
      "该2.29844e+18在老年人广东渗透率为nan\n",
      "该1.51597e+18在老年人江苏渗透率为nan\n",
      "该2.27395e+18在老年人江苏渗透率为nan\n",
      "该2.29844e+18在老年人江苏渗透率为nan\n",
      "该1.51597e+18在老年人浙江渗透率为nan\n",
      "该2.27395e+18在老年人浙江渗透率为nan\n",
      "该2.29844e+18在老年人浙江渗透率为nan\n",
      "该1.51597e+18在老年人海南渗透率为nan\n",
      "该2.27395e+18在老年人海南渗透率为nan\n",
      "该2.29844e+18在老年人海南渗透率为nan\n",
      "该1.51597e+18在老年人湖北渗透率为nan\n",
      "该2.27395e+18在老年人湖北渗透率为nan\n",
      "该2.29844e+18在老年人湖北渗透率为nan\n",
      "该1.51597e+18在老年人湖南渗透率为nan\n",
      "该2.27395e+18在老年人湖南渗透率为nan\n",
      "该2.29844e+18在老年人湖南渗透率为nan\n",
      "该1.51597e+18在老年人重庆渗透率为nan\n",
      "该2.27395e+18在老年人重庆渗透率为nan\n",
      "该2.29844e+18在老年人重庆渗透率为nan\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Administrator\\AppData\\Local\\Temp\\ipykernel_12100\\2074523314.py:2: RuntimeWarning: invalid value encountered in true_divide\n",
      "  print(\"该{}在{}渗透率为{}\".format(i[2],i[0]+i[1],(result4.loc[i].values/result5.loc[(i[0],i[1])].values)[0]))\n"
     ]
    }
   ],
   "source": [
    "for i in result4.index:\n",
    "\tprint(\"该{}在{}渗透率为{}\".format(i[2],i[0]+i[1],(result4.loc[i].values/result5.loc[(i[0],i[1])].values)[0]))\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "outputs": [
    {
     "data": {
      "text/plain": "weekday\n0     904\n1     892\n2    1030\n3    1388\n4    1149\n5    1168\n6     803\nName: uid, dtype: int64"
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 客单价 = 总金额/总人数(去重)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "outputs": [
    {
     "data": {
      "text/plain": "         id                start_time           oid           pid  \\\n0         0 2020-04-24 11:50:39+00:00  2.294360e+18  1.515970e+18   \n1         1 2020-04-24 11:50:39+00:00  2.294360e+18  1.515970e+18   \n2         2 2020-04-24 14:37:43+00:00  2.294440e+18  2.273950e+18   \n3         3 2020-04-24 14:37:43+00:00  2.294440e+18  2.273950e+18   \n5         5 2020-04-26 08:45:57+00:00  2.295720e+18  1.515970e+18   \n...     ...                       ...           ...           ...   \n9993   9999 2020-05-17 08:20:21+00:00  2.310920e+18  1.515970e+18   \n9994  10000 2020-05-17 08:21:05+00:00  2.310920e+18  1.515970e+18   \n9995  10001 2020-05-17 08:21:59+00:00  2.310920e+18  2.273950e+18   \n9996  10002 2020-05-17 08:23:45+00:00  2.310930e+18  2.273950e+18   \n9997  10003 2020-05-17 08:24:07+00:00  2.310930e+18  1.515970e+18   \n\n           type_id                           type    brand   price  \\\n0     2.270000e+18             electronics.tablet  samsung  162.01   \n1     2.270000e+18             electronics.tablet  samsung  162.01   \n2     2.270000e+18    electronics.audio.headphone   huawei   77.52   \n3     2.270000e+18    electronics.audio.headphone   huawei   77.52   \n5     2.270000e+18        furniture.kitchen.table  maestro   39.33   \n...            ...                            ...      ...     ...   \n9993  2.270000e+18             computers.notebook   lenovo  370.35   \n9994  2.270000e+18  appliances.environment.vacuum   arnica   60.86   \n9995  2.270000e+18     appliances.environment.fan  polaris   20.81   \n9996  2.370000e+18           electronics.video.tv  samsung  416.64   \n9997  2.270000e+18             computers.notebook     asus  509.24   \n\n                    uid  age sex province  weekday age_box  \n0      1.52e+1882716832   24   女       海南        4     年轻人  \n1     1.52e+18827110215   24   女       海南        4     年轻人  \n2       1.52e+188271192   38   女       北京        4     青年人  \n3        1.52e+18827132   38   女       北京        4     青年人  \n5      1.52e+1882714581   20   男       重庆        6     年轻人  \n...                 ...  ...  ..      ...      ...     ...  \n9993   1.52e+1882715402   31   男       江苏        6     青年人  \n9994   1.52e+1882716812   19   女       上海        6     年轻人  \n9995   1.52e+1882715317   24   男       广东        6     年轻人  \n9996  1.52e+18827110120   25   女       广东        6     年轻人  \n9997   1.52e+1882718800   23   男       浙江        6     年轻人  \n\n[7757 rows x 14 columns]",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>id</th>\n      <th>start_time</th>\n      <th>oid</th>\n      <th>pid</th>\n      <th>type_id</th>\n      <th>type</th>\n      <th>brand</th>\n      <th>price</th>\n      <th>uid</th>\n      <th>age</th>\n      <th>sex</th>\n      <th>province</th>\n      <th>weekday</th>\n      <th>age_box</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>2020-04-24 11:50:39+00:00</td>\n      <td>2.294360e+18</td>\n      <td>1.515970e+18</td>\n      <td>2.270000e+18</td>\n      <td>electronics.tablet</td>\n      <td>samsung</td>\n      <td>162.01</td>\n      <td>1.52e+1882716832</td>\n      <td>24</td>\n      <td>女</td>\n      <td>海南</td>\n      <td>4</td>\n      <td>年轻人</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>2020-04-24 11:50:39+00:00</td>\n      <td>2.294360e+18</td>\n      <td>1.515970e+18</td>\n      <td>2.270000e+18</td>\n      <td>electronics.tablet</td>\n      <td>samsung</td>\n      <td>162.01</td>\n      <td>1.52e+18827110215</td>\n      <td>24</td>\n      <td>女</td>\n      <td>海南</td>\n      <td>4</td>\n      <td>年轻人</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2</td>\n      <td>2020-04-24 14:37:43+00:00</td>\n      <td>2.294440e+18</td>\n      <td>2.273950e+18</td>\n      <td>2.270000e+18</td>\n      <td>electronics.audio.headphone</td>\n      <td>huawei</td>\n      <td>77.52</td>\n      <td>1.52e+188271192</td>\n      <td>38</td>\n      <td>女</td>\n      <td>北京</td>\n      <td>4</td>\n      <td>青年人</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>3</td>\n      <td>2020-04-24 14:37:43+00:00</td>\n      <td>2.294440e+18</td>\n      <td>2.273950e+18</td>\n      <td>2.270000e+18</td>\n      <td>electronics.audio.headphone</td>\n      <td>huawei</td>\n      <td>77.52</td>\n      <td>1.52e+18827132</td>\n      <td>38</td>\n      <td>女</td>\n      <td>北京</td>\n      <td>4</td>\n      <td>青年人</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>5</td>\n      <td>2020-04-26 08:45:57+00:00</td>\n      <td>2.295720e+18</td>\n      <td>1.515970e+18</td>\n      <td>2.270000e+18</td>\n      <td>furniture.kitchen.table</td>\n      <td>maestro</td>\n      <td>39.33</td>\n      <td>1.52e+1882714581</td>\n      <td>20</td>\n      <td>男</td>\n      <td>重庆</td>\n      <td>6</td>\n      <td>年轻人</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>9993</th>\n      <td>9999</td>\n      <td>2020-05-17 08:20:21+00:00</td>\n      <td>2.310920e+18</td>\n      <td>1.515970e+18</td>\n      <td>2.270000e+18</td>\n      <td>computers.notebook</td>\n      <td>lenovo</td>\n      <td>370.35</td>\n      <td>1.52e+1882715402</td>\n      <td>31</td>\n      <td>男</td>\n      <td>江苏</td>\n      <td>6</td>\n      <td>青年人</td>\n    </tr>\n    <tr>\n      <th>9994</th>\n      <td>10000</td>\n      <td>2020-05-17 08:21:05+00:00</td>\n      <td>2.310920e+18</td>\n      <td>1.515970e+18</td>\n      <td>2.270000e+18</td>\n      <td>appliances.environment.vacuum</td>\n      <td>arnica</td>\n      <td>60.86</td>\n      <td>1.52e+1882716812</td>\n      <td>19</td>\n      <td>女</td>\n      <td>上海</td>\n      <td>6</td>\n      <td>年轻人</td>\n    </tr>\n    <tr>\n      <th>9995</th>\n      <td>10001</td>\n      <td>2020-05-17 08:21:59+00:00</td>\n      <td>2.310920e+18</td>\n      <td>2.273950e+18</td>\n      <td>2.270000e+18</td>\n      <td>appliances.environment.fan</td>\n      <td>polaris</td>\n      <td>20.81</td>\n      <td>1.52e+1882715317</td>\n      <td>24</td>\n      <td>男</td>\n      <td>广东</td>\n      <td>6</td>\n      <td>年轻人</td>\n    </tr>\n    <tr>\n      <th>9996</th>\n      <td>10002</td>\n      <td>2020-05-17 08:23:45+00:00</td>\n      <td>2.310930e+18</td>\n      <td>2.273950e+18</td>\n      <td>2.370000e+18</td>\n      <td>electronics.video.tv</td>\n      <td>samsung</td>\n      <td>416.64</td>\n      <td>1.52e+18827110120</td>\n      <td>25</td>\n      <td>女</td>\n      <td>广东</td>\n      <td>6</td>\n      <td>年轻人</td>\n    </tr>\n    <tr>\n      <th>9997</th>\n      <td>10003</td>\n      <td>2020-05-17 08:24:07+00:00</td>\n      <td>2.310930e+18</td>\n      <td>1.515970e+18</td>\n      <td>2.270000e+18</td>\n      <td>computers.notebook</td>\n      <td>asus</td>\n      <td>509.24</td>\n      <td>1.52e+1882718800</td>\n      <td>23</td>\n      <td>男</td>\n      <td>浙江</td>\n      <td>6</td>\n      <td>年轻人</td>\n    </tr>\n  </tbody>\n</table>\n<p>7757 rows × 14 columns</p>\n</div>"
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1[\"uid\"] = df1[\"uid\"].astype(\"string\")\n",
    "df1[\"uid\"] = df1[\"uid\"].map(lambda x:x+str(random.randint(1,1000)))\n",
    "df1"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "outputs": [
    {
     "data": {
      "text/plain": "             price   uid\nweekday                 \n0        173271.98   904\n1        139998.56   892\n2        179505.40  1030\n3        265631.89  1388\n4        240679.77  1149\n5        242984.07  1168\n6        173666.08   803",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>price</th>\n      <th>uid</th>\n    </tr>\n    <tr>\n      <th>weekday</th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>173271.98</td>\n      <td>904</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>139998.56</td>\n      <td>892</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>179505.40</td>\n      <td>1030</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>265631.89</td>\n      <td>1388</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>240679.77</td>\n      <td>1149</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>242984.07</td>\n      <td>1168</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>173666.08</td>\n      <td>803</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result6 = df1.groupby(\"weekday\")[\"price\"].sum()\n",
    "result7 = df1.groupby(\"weekday\")[\"uid\"].nunique()\n",
    "\n",
    "result8=pd.merge(result6,result7,left_index=True,right_index=True)\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "outputs": [
    {
     "data": {
      "text/plain": "             price   uid  unit_price\nweekday                             \n0        173271.98   904  191.672544\n1        139998.56   892  156.949058\n2        179505.40  1030  174.277087\n3        265631.89  1388  191.377442\n4        240679.77  1149  209.468903\n5        242984.07  1168  208.034307\n6        173666.08   803  216.271582",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>price</th>\n      <th>uid</th>\n      <th>unit_price</th>\n    </tr>\n    <tr>\n      <th>weekday</th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>173271.98</td>\n      <td>904</td>\n      <td>191.672544</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>139998.56</td>\n      <td>892</td>\n      <td>156.949058</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>179505.40</td>\n      <td>1030</td>\n      <td>174.277087</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>265631.89</td>\n      <td>1388</td>\n      <td>191.377442</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>240679.77</td>\n      <td>1149</td>\n      <td>209.468903</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>242984.07</td>\n      <td>1168</td>\n      <td>208.034307</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>173666.08</td>\n      <td>803</td>\n      <td>216.271582</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result8[\"unit_price\"] =  result8[\"price\"]/result8[\"uid\"]\n",
    "result8"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "outputs": [
    {
     "data": {
      "text/plain": "(array([1., 0., 1., 0., 0., 2., 0., 0., 2., 1.]),\n array([156.9490583 , 162.88131062, 168.81356295, 174.74581528,\n        180.67806761, 186.61031993, 192.54257226, 198.47482459,\n        204.40707691, 210.33932924, 216.27158157]),\n <BarContainer object of 10 artists>)"
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(result8[\"unit_price\"],color=\"yellow\")\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "outputs": [
    {
     "data": {
      "text/plain": "'采购单号\\t         送货曰期\\t商品\\t订单数量\\t单价\\n2023022301\\t2023/2/23\\t鞋\\t3405\\t106\\n\\n2023022302\\t2023/2/24\\t帽\\t1742\\t247\\n\\n2023022303\\t2023/2/25\\t袜子\\t3972\\t261\\n2023022304\\t2023/2/26\\t手套\\t3031\\t261\\n2023022310\\t2023/2/27\\t围巾\\t2123\\t137\\n2023022306\\t2023/2/28\\t领带\\t2647\\t51\\n'"
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f = open(\"C:\\\\Users\\\\Administrator\\\\Desktop\\\\Python数据分析\\\\月考题\\\\大数据 大数据系 专高5 《Python数据分析EDA》月考题库（修改2）\\\\3-2 大数据 大数据系 专高5 《Python数据分析EDA》月考题库（新建）-已入库\\\\3-2 大数据 大数据系 专高5 《Python数据分析EDA》月考题库（新建）-已入库\\\\02-02-5-00006\\\\02-02-5-00006\\\\数据源\\\\purchase.txt\",\"r\",encoding=\"gbk\")\n",
    "content = f.read()\n",
    "content"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "outputs": [],
   "source": [
    "import os\n",
    "os.makedirs(\"student/yuekao\")\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "import shutil\n",
    "def removey_any(dir):\n",
    "\tshutil.rmtree(dir)\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "outputs": [
    {
     "data": {
      "text/plain": "               id       oid       pid   type_id     price       age   weekday\nid       1.000000  0.997790 -0.027518  0.042114  0.047359 -0.028068 -0.023523\noid      0.997790  1.000000 -0.030237  0.042259  0.050118 -0.027642 -0.023347\npid     -0.027518 -0.030237  1.000000  0.036081 -0.123959 -0.016071 -0.030087\ntype_id  0.042114  0.042259  0.036081  1.000000  0.144893 -0.019688 -0.013649\nprice    0.047359  0.050118 -0.123959  0.144893  1.000000 -0.030146  0.048000\nage     -0.028068 -0.027642 -0.016071 -0.019688 -0.030146  1.000000  0.003405\nweekday -0.023523 -0.023347 -0.030087 -0.013649  0.048000  0.003405  1.000000",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>id</th>\n      <th>oid</th>\n      <th>pid</th>\n      <th>type_id</th>\n      <th>price</th>\n      <th>age</th>\n      <th>weekday</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>id</th>\n      <td>1.000000</td>\n      <td>0.997790</td>\n      <td>-0.027518</td>\n      <td>0.042114</td>\n      <td>0.047359</td>\n      <td>-0.028068</td>\n      <td>-0.023523</td>\n    </tr>\n    <tr>\n      <th>oid</th>\n      <td>0.997790</td>\n      <td>1.000000</td>\n      <td>-0.030237</td>\n      <td>0.042259</td>\n      <td>0.050118</td>\n      <td>-0.027642</td>\n      <td>-0.023347</td>\n    </tr>\n    <tr>\n      <th>pid</th>\n      <td>-0.027518</td>\n      <td>-0.030237</td>\n      <td>1.000000</td>\n      <td>0.036081</td>\n      <td>-0.123959</td>\n      <td>-0.016071</td>\n      <td>-0.030087</td>\n    </tr>\n    <tr>\n      <th>type_id</th>\n      <td>0.042114</td>\n      <td>0.042259</td>\n      <td>0.036081</td>\n      <td>1.000000</td>\n      <td>0.144893</td>\n      <td>-0.019688</td>\n      <td>-0.013649</td>\n    </tr>\n    <tr>\n      <th>price</th>\n      <td>0.047359</td>\n      <td>0.050118</td>\n      <td>-0.123959</td>\n      <td>0.144893</td>\n      <td>1.000000</td>\n      <td>-0.030146</td>\n      <td>0.048000</td>\n    </tr>\n    <tr>\n      <th>age</th>\n      <td>-0.028068</td>\n      <td>-0.027642</td>\n      <td>-0.016071</td>\n      <td>-0.019688</td>\n      <td>-0.030146</td>\n      <td>1.000000</td>\n      <td>0.003405</td>\n    </tr>\n    <tr>\n      <th>weekday</th>\n      <td>-0.023523</td>\n      <td>-0.023347</td>\n      <td>-0.030087</td>\n      <td>-0.013649</td>\n      <td>0.048000</td>\n      <td>0.003405</td>\n      <td>1.000000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 176,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.corr()\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%z\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "outputs": [
    {
     "data": {
      "text/plain": "         id                start_time           oid           pid  \\\n0         0 2020-04-24 11:50:39+00:00  2.294360e+18  1.515970e+18   \n1         1 2020-04-24 11:50:39+00:00  2.294360e+18  1.515970e+18   \n2         2 2020-04-24 14:37:43+00:00  2.294440e+18  2.273950e+18   \n3         3 2020-04-24 14:37:43+00:00  2.294440e+18  2.273950e+18   \n5         5 2020-04-26 08:45:57+00:00  2.295720e+18  1.515970e+18   \n...     ...                       ...           ...           ...   \n9993   9999 2020-05-17 08:20:21+00:00  2.310920e+18  1.515970e+18   \n9994  10000 2020-05-17 08:21:05+00:00  2.310920e+18  1.515970e+18   \n9995  10001 2020-05-17 08:21:59+00:00  2.310920e+18  2.273950e+18   \n9996  10002 2020-05-17 08:23:45+00:00  2.310930e+18  2.273950e+18   \n9997  10003 2020-05-17 08:24:07+00:00  2.310930e+18  1.515970e+18   \n\n           type_id                           type    brand   price  \\\n0     2.270000e+18             electronics.tablet  samsung  162.01   \n1     2.270000e+18             electronics.tablet  samsung  162.01   \n2     2.270000e+18    electronics.audio.headphone   huawei   77.52   \n3     2.270000e+18    electronics.audio.headphone   huawei   77.52   \n5     2.270000e+18        furniture.kitchen.table  maestro   39.33   \n...            ...                            ...      ...     ...   \n9993  2.270000e+18             computers.notebook   lenovo  370.35   \n9994  2.270000e+18  appliances.environment.vacuum   arnica   60.86   \n9995  2.270000e+18     appliances.environment.fan  polaris   20.81   \n9996  2.370000e+18           electronics.video.tv  samsung  416.64   \n9997  2.270000e+18             computers.notebook     asus  509.24   \n\n                    uid  age sex province  weekday age_box  label  \n0      1.52e+1882716832   24   女       海南        4     年轻人      1  \n1     1.52e+18827110215   24   女       海南        4     年轻人      1  \n2       1.52e+188271192   38   女       北京        4     青年人      0  \n3        1.52e+18827132   38   女       北京        4     青年人      1  \n5      1.52e+1882714581   20   男       重庆        6     年轻人      0  \n...                 ...  ...  ..      ...      ...     ...    ...  \n9993   1.52e+1882715402   31   男       江苏        6     青年人      1  \n9994   1.52e+1882716812   19   女       上海        6     年轻人      1  \n9995   1.52e+1882715317   24   男       广东        6     年轻人      0  \n9996  1.52e+18827110120   25   女       广东        6     年轻人      0  \n9997   1.52e+1882718800   23   男       浙江        6     年轻人      1  \n\n[7757 rows x 15 columns]",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>id</th>\n      <th>start_time</th>\n      <th>oid</th>\n      <th>pid</th>\n      <th>type_id</th>\n      <th>type</th>\n      <th>brand</th>\n      <th>price</th>\n      <th>uid</th>\n      <th>age</th>\n      <th>sex</th>\n      <th>province</th>\n      <th>weekday</th>\n      <th>age_box</th>\n      <th>label</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>2020-04-24 11:50:39+00:00</td>\n      <td>2.294360e+18</td>\n      <td>1.515970e+18</td>\n      <td>2.270000e+18</td>\n      <td>electronics.tablet</td>\n      <td>samsung</td>\n      <td>162.01</td>\n      <td>1.52e+1882716832</td>\n      <td>24</td>\n      <td>女</td>\n      <td>海南</td>\n      <td>4</td>\n      <td>年轻人</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>2020-04-24 11:50:39+00:00</td>\n      <td>2.294360e+18</td>\n      <td>1.515970e+18</td>\n      <td>2.270000e+18</td>\n      <td>electronics.tablet</td>\n      <td>samsung</td>\n      <td>162.01</td>\n      <td>1.52e+18827110215</td>\n      <td>24</td>\n      <td>女</td>\n      <td>海南</td>\n      <td>4</td>\n      <td>年轻人</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2</td>\n      <td>2020-04-24 14:37:43+00:00</td>\n      <td>2.294440e+18</td>\n      <td>2.273950e+18</td>\n      <td>2.270000e+18</td>\n      <td>electronics.audio.headphone</td>\n      <td>huawei</td>\n      <td>77.52</td>\n      <td>1.52e+188271192</td>\n      <td>38</td>\n      <td>女</td>\n      <td>北京</td>\n      <td>4</td>\n      <td>青年人</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>3</td>\n      <td>2020-04-24 14:37:43+00:00</td>\n      <td>2.294440e+18</td>\n      <td>2.273950e+18</td>\n      <td>2.270000e+18</td>\n      <td>electronics.audio.headphone</td>\n      <td>huawei</td>\n      <td>77.52</td>\n      <td>1.52e+18827132</td>\n      <td>38</td>\n      <td>女</td>\n      <td>北京</td>\n      <td>4</td>\n      <td>青年人</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>5</td>\n      <td>2020-04-26 08:45:57+00:00</td>\n      <td>2.295720e+18</td>\n      <td>1.515970e+18</td>\n      <td>2.270000e+18</td>\n      <td>furniture.kitchen.table</td>\n      <td>maestro</td>\n      <td>39.33</td>\n      <td>1.52e+1882714581</td>\n      <td>20</td>\n      <td>男</td>\n      <td>重庆</td>\n      <td>6</td>\n      <td>年轻人</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>9993</th>\n      <td>9999</td>\n      <td>2020-05-17 08:20:21+00:00</td>\n      <td>2.310920e+18</td>\n      <td>1.515970e+18</td>\n      <td>2.270000e+18</td>\n      <td>computers.notebook</td>\n      <td>lenovo</td>\n      <td>370.35</td>\n      <td>1.52e+1882715402</td>\n      <td>31</td>\n      <td>男</td>\n      <td>江苏</td>\n      <td>6</td>\n      <td>青年人</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>9994</th>\n      <td>10000</td>\n      <td>2020-05-17 08:21:05+00:00</td>\n      <td>2.310920e+18</td>\n      <td>1.515970e+18</td>\n      <td>2.270000e+18</td>\n      <td>appliances.environment.vacuum</td>\n      <td>arnica</td>\n      <td>60.86</td>\n      <td>1.52e+1882716812</td>\n      <td>19</td>\n      <td>女</td>\n      <td>上海</td>\n      <td>6</td>\n      <td>年轻人</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>9995</th>\n      <td>10001</td>\n      <td>2020-05-17 08:21:59+00:00</td>\n      <td>2.310920e+18</td>\n      <td>2.273950e+18</td>\n      <td>2.270000e+18</td>\n      <td>appliances.environment.fan</td>\n      <td>polaris</td>\n      <td>20.81</td>\n      <td>1.52e+1882715317</td>\n      <td>24</td>\n      <td>男</td>\n      <td>广东</td>\n      <td>6</td>\n      <td>年轻人</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>9996</th>\n      <td>10002</td>\n      <td>2020-05-17 08:23:45+00:00</td>\n      <td>2.310930e+18</td>\n      <td>2.273950e+18</td>\n      <td>2.370000e+18</td>\n      <td>electronics.video.tv</td>\n      <td>samsung</td>\n      <td>416.64</td>\n      <td>1.52e+18827110120</td>\n      <td>25</td>\n      <td>女</td>\n      <td>广东</td>\n      <td>6</td>\n      <td>年轻人</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>9997</th>\n      <td>10003</td>\n      <td>2020-05-17 08:24:07+00:00</td>\n      <td>2.310930e+18</td>\n      <td>1.515970e+18</td>\n      <td>2.270000e+18</td>\n      <td>computers.notebook</td>\n      <td>asus</td>\n      <td>509.24</td>\n      <td>1.52e+1882718800</td>\n      <td>23</td>\n      <td>男</td>\n      <td>浙江</td>\n      <td>6</td>\n      <td>年轻人</td>\n      <td>1</td>\n    </tr>\n  </tbody>\n</table>\n<p>7757 rows × 15 columns</p>\n</div>"
     },
     "execution_count": 184,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# https://blog.csdn.net/wlddhj/article/details/139012078\n",
    "label = [random.randint(0,1) for i in df1.index]\n",
    "df1[\"label\"] = label\n",
    "df1"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "outputs": [
    {
     "data": {
      "text/plain": "      age   price\n6499   48   17.34\n6127   27   18.50\n3321   39   33.54\n4594   16   92.57\n8484   37  124.98\n...   ...     ...\n6766   47  196.74\n6975   32  109.93\n1149   16   12.71\n9794   32  196.27\n9361   19  203.68\n\n[6205 rows x 2 columns]",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>age</th>\n      <th>price</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>6499</th>\n      <td>48</td>\n      <td>17.34</td>\n    </tr>\n    <tr>\n      <th>6127</th>\n      <td>27</td>\n      <td>18.50</td>\n    </tr>\n    <tr>\n      <th>3321</th>\n      <td>39</td>\n      <td>33.54</td>\n    </tr>\n    <tr>\n      <th>4594</th>\n      <td>16</td>\n      <td>92.57</td>\n    </tr>\n    <tr>\n      <th>8484</th>\n      <td>37</td>\n      <td>124.98</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>6766</th>\n      <td>47</td>\n      <td>196.74</td>\n    </tr>\n    <tr>\n      <th>6975</th>\n      <td>32</td>\n      <td>109.93</td>\n    </tr>\n    <tr>\n      <th>1149</th>\n      <td>16</td>\n      <td>12.71</td>\n    </tr>\n    <tr>\n      <th>9794</th>\n      <td>32</td>\n      <td>196.27</td>\n    </tr>\n    <tr>\n      <th>9361</th>\n      <td>19</td>\n      <td>203.68</td>\n    </tr>\n  </tbody>\n</table>\n<p>6205 rows × 2 columns</p>\n</div>"
     },
     "execution_count": 189,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = df1[['age',\"price\"]]\n",
    "y = df1['label']\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "\n",
    "\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "outputs": [
    {
     "data": {
      "text/plain": "LogisticRegression(max_iter=200)",
      "text/html": "<style>#sk-container-id-1 {color: black;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression(max_iter=200)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression(max_iter=200)</pre></div></div></div></div></div>"
     },
     "execution_count": 192,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr = LogisticRegression(max_iter=200)\n",
    "lr.fit(X_train,y_train)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "outputs": [
    {
     "data": {
      "text/plain": "RandomForestClassifier(min_samples_leaf=2, min_samples_split=3)",
      "text/html": "<style>#sk-container-id-2 {color: black;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(min_samples_leaf=2, min_samples_split=3)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(min_samples_leaf=2, min_samples_split=3)</pre></div></div></div></div></div>"
     },
     "execution_count": 193,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf = RandomForestClassifier(min_samples_split=3,min_samples_leaf=2)\n",
    "rf.fit(X_train,y_train)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "outputs": [
    {
     "data": {
      "text/plain": "array([0, 1, 0, ..., 1, 1, 0], dtype=int64)"
     },
     "execution_count": 194,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr_pred = lr.predict(X_test)\n",
    "lr_pred"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "outputs": [
    {
     "data": {
      "text/plain": "array([0, 1, 0, ..., 1, 1, 1], dtype=int64)"
     },
     "execution_count": 195,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_pred = rf.predict(X_test)\n",
    "rf_pred"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "outputs": [],
   "source": [
    "as1 = accuracy_score(y_test,lr_pred)\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "outputs": [],
   "source": [
    "as2 = accuracy_score(y_test,rf_pred)\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "outputs": [],
   "source": [
    "from pandas import DataFrame\n",
    "if as1 > as2:\n",
    "\tresult = lr.predict(X_test)\n",
    "\tdf1 = DataFrame(result)\n",
    "\tdf1.to_csv(\"1.csv\")\n",
    "else:\n",
    "\tresult = rf.predict(X_test)\n",
    "\tdf2 = DataFrame(result)\n",
    "\tdf2.to_csv(\"2.csv\")"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  }
 ],
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